
import os

import pandas as pd
import numpy as np

from datetime import datetime

import glob

import shutil

import sqlite3

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.pipeline import make_pipeline

import scipy.stats as stats
from scipy.stats import ttest_ind

from scipy.stats import f_oneway
from scipy.stats import chi2_contingency
from statsmodels.stats.proportion import proportions_ztest
from scipy.stats import ttest_1samp
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from scipy.stats import pointbiserialr

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression

from sklearn.metrics import accuracy_score

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from mpl_toolkits.mplot3d import Axes3D

from statsmodels.formula.api import ols
import statsmodels.api as sm

from sklearn import linear_model

from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import OneHotEncoder

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer


os.getcwd()

'/Users/michaelbyrd/Desktop/MIS556'


# Specify the path of the directory you want to change to
new_directory = "/Users/michaelbyrd/Desktop/MIS556/stats"

# Change the current working directory
os.chdir(new_directory)

# Display Directory
os.getcwd()

'/Users/michaelbyrd/Desktop/MIS556/stats'


# Display All Columns

pd.set_option('display.max_columns', None)


# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)


# Display All Columns

pd.set_option('display.max_columns', None)


# Function for Complete Breakdown
def breakdown(df):
    # print("Head", "\n\n\n", df.head())
    # print("Tail", "\n\n\n", df.tail())
    # print("\n\n\n", "Columns", "\n", df.columns)
    # print("\n\n\n", "Describe", "\n", df.describe())
    # print("\n\n\n", "Correlation Matrix", "\n", df.corr())
    # print("\n\n\n", "Info", "\n", df.info())
    print("Distinct Count", "\n", df.nunique())
    print("\n\n\n", "Count", "\n", df.count())
    print("\n\n\n", "DTypes", "\n", df.dtypes)
    print("\n\n\n", "Shape", "\n", df.shape)
    print("\n\n\n", "Nulls", "\n", df.isna().sum())
    print("\n\n\n", "Null %", "\n", round(df.isna().sum()/len(df)*100, 2))
    # print("\n\n\n", "Mean", "\n\n\n", df.mean())
    # print("\n\n\n", "Median", "\n\n\n", df.median())
    # print("\n\n\n", "Standard Deviation", "\n\n\n", df.std())
    # print("\n\n\n", "Max", "\n\n\n", df.max())
    # print("\n\n\n", "Min", "\n\n\n", df.min())


# Function to convert time format to float representing minutes
def convert_to_minutes(time_str):
    if isinstance(time_str, float):
        return time_str  # Return the float value directly
    else:
        minutes, seconds = map(int, time_str.split(':'))
        return minutes + seconds / 60.0


def find_duplicates_in_column(df, column_name):
    """
    Find and display a list of duplicates in a specified column of a DataFrame.

    Parameters:
    - df: DataFrame
        The DataFrame containing the column with potential duplicates.
    - column_name: str
        The name of the column to search for duplicates.

    Returns:
    - list
        A list containing unique duplicate values found in the specified column.
    """
    # Find duplicates in the specified column
    duplicate_values = df[df.duplicated(subset=[column_name], keep=False)]

    # Extract the list of duplicate values
    duplicate_list = duplicate_values[column_name].unique().tolist()

    return duplicate_list


def convert_inches(height):
    """
    Convert height from feet'inches" format to inches.

    Parameters:
    - height: str
        Height value in the format feet'inches" (e.g., "5'10").

    Returns:
    - int
        Height in inches.
    """
    if pd.isna(height):  # Handling NaN values
        return height
    
    feet, inches = height.split("'")
    total_inches = int(feet) * 12 + int(inches.strip('"'))
    return total_inches


def unique_column_values(dataframe, column_name):
    """
    Returns a list of unique values in the specified column of a DataFrame.
    
    Args:
    - dataframe: pandas DataFrame containing the data
    - column_name: name of the column
    
    Returns:
    - unique_values: list of unique values in the specified column
    """
    unique_values = dataframe[column_name].unique().tolist()
    return unique_values


# Define the database file name
db_file = 'mma.db'

# Connect to the SQLite database
conn = sqlite3.connect(db_file)

# Create a cursor object to execute SQL queries
cursor = conn.cursor()


# Execute SQL query to get all table names in the database
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")

# Fetch all the table names
table_names = cursor.fetchall()

# Print the table names
print("Tables in the database:")
for name in table_names:
    print(name[0])

Tables in the database:
master
fighter_buyrate
event_buyrate
frame
career
fight_average
round_average
records
heavyweight
light_heavyweight
middleweight
welterweight
lightweight
featherweight
bantamweight
flyweight
women_featherweight
women_bantamweight
women_flyweight
women_strawweight


len(table_names)

20


connection = sqlite3.connect(db_file)


master = pd.read_sql('SELECT * FROM master', connection)
master


fighter_buyrate = pd.read_sql('SELECT * FROM fighter_buyrate', connection)

# Sorting by Buyrate from highest to lowest
fighter_buyrate = fighter_buyrate.sort_values(by='Buyrate', ascending=False).reset_index(drop=True)

fighter_buyrate


fb = fighter_buyrate[fighter_buyrate['Buyrate'] > 0]
# fb = fighter_buyrate[fighter_buyrate['PPV_Fights'] > 1]
# fb = fb.dropna(subset=['Buyrate'])
# fb = fighter_buyrate

# Calculate the counts for the top 2%, 5%, and 10% of the dataset
top_1_percent_count = int(len(fb) * 0.01)
top_10_percent_count = int(len(fb) * 0.1)

# Sort the dataset by 'Buyrate' descending
fb_sorted_by_buyrate = fb.sort_values(by='Buyrate', ascending=False)

# Extract the top 1% and 10% earners
top_1_percent_earners = fb_sorted_by_buyrate.head(top_1_percent_count)
top_10_percent_earners = fb_sorted_by_buyrate.head(top_10_percent_count)

# Convert to lists for display
top_1_percent_list = top_1_percent_earners['FIGHTER'].tolist()
top_10_percent_list = top_10_percent_earners['FIGHTER'].tolist()


top_1_percent_earners


top_10_percent_earners


event_buyrate = pd.read_sql('SELECT * FROM event_buyrate', connection)

event_buyrate = event_buyrate[event_buyrate['Buyrate'] > 0]
event_buyrate


frame = pd.read_sql('SELECT * FROM frame', connection)
frame


career = pd.read_sql('SELECT * FROM career', connection)
career


fight_average = pd.read_sql('SELECT * FROM fight_average', connection)
fight_average


round_average = pd.read_sql('SELECT * FROM round_average', connection)
round_average


records = pd.read_sql('SELECT * FROM records', connection)
records


# Filter rows where 'Total_Fights' is greater than 3
min_fights = records[records['Total_Fights'] > 3]

# Create a list of fighters with more than 3 fights
fighters_list = min_fights['FIGHTER'].tolist()

len(fighters_list)

1321


heavyweight = pd.read_sql('SELECT * FROM heavyweight', connection)
light_heavyweight = pd.read_sql('SELECT * FROM light_heavyweight', connection)
middleweight = pd.read_sql('SELECT * FROM middleweight', connection)
welterweight = pd.read_sql('SELECT * FROM welterweight', connection)
lightweight = pd.read_sql('SELECT * FROM lightweight', connection)
featherweight = pd.read_sql('SELECT * FROM featherweight', connection)
bantamweight = pd.read_sql('SELECT * FROM bantamweight', connection)
flyweight = pd.read_sql('SELECT * FROM flyweight', connection)
women_featherweight = pd.read_sql('SELECT * FROM women_featherweight', connection)
women_bantamweight = pd.read_sql('SELECT * FROM women_bantamweight', connection)
women_flyweight = pd.read_sql('SELECT * FROM women_flyweight', connection)
women_strawweight = pd.read_sql('SELECT * FROM women_strawweight', connection)


# Create a new subset with the specified columns
df = master[['STANCE', 'RESULT', 'OPP_STANCE']]

# Remove rows where 'STANCE' or 'OPP_STANCE' is null
df = df.dropna(subset=['STANCE', 'OPP_STANCE'])


# Display the first few rows of the new subset to confirm
df


df = df[df['STANCE'] != df['OPP_STANCE']]

df = df[df['RESULT'].isin(['Win', 'Loss'])]

df


# Assuming df is your DataFrame after filtering to include only 'Win' and 'Loss' in 'RESULT'
result_counts = df.groupby(['STANCE', 'RESULT']).size().unstack(fill_value=0)

# Calculate the total fights per stance
result_counts['Total'] = result_counts.sum(axis=1)

# Calculate percentages
result_counts['Win_Percentage'] = (result_counts['Win'] / result_counts['Total']) * 100
result_counts['Loss_Percentage'] = (result_counts['Loss'] / result_counts['Total']) * 100

# You can then display this DataFrame
print(result_counts[['Win', 'Loss', 'Win_Percentage', 'Loss_Percentage']])

RESULT     Win  Loss  Win_Percentage  Loss_Percentage
STANCE                                               
Orthodox  2705  3051       46.994441        53.005559
Southpaw  2635  2379       52.552852        47.447148
Switch     749   659       53.196023        46.803977


# Select specific columns and remove duplicates
subset_df = master[['BOUT', 'RESULT', 'FRAME_DELTA']].drop_duplicates().dropna(subset=['FRAME_DELTA'])

# Create ADVANTAGE column based on FRAME_DELTA
subset_df['ADVANTAGE'] = subset_df['FRAME_DELTA'].apply(lambda x: 'Yes' if x > 0 else 'No')

subset_df = subset_df[subset_df['RESULT'].isin(['Win', 'Loss'])]

# Output to verify the changes
subset_df


# Assuming df is your DataFrame after filtering to include only 'Win' and 'Loss' in 'RESULT'
result_counts = subset_df.groupby(['ADVANTAGE', 'RESULT']).size().unstack(fill_value=0)

# Calculate the total fights per stance
result_counts['Total'] = result_counts.sum(axis=1)

# Calculate percentages
result_counts['Win_Percentage'] = (result_counts['Win'] / result_counts['Total']) * 100
result_counts['Loss_Percentage'] = (result_counts['Loss'] / result_counts['Total']) * 100

# You can then display this DataFrame
print(result_counts[['Win', 'Loss', 'Win_Percentage', 'Loss_Percentage']])

RESULT      Win  Loss  Win_Percentage  Loss_Percentage
ADVANTAGE                                             
No         3503  3958       46.950811        53.049189
Yes        3379  2924       53.609392        46.390608


# Select specific columns and remove duplicates
subset_df = master[['BOUT', 'RESULT', 'APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA',  'FRAME_DELTA']].drop_duplicates()

subset_df


# Filter the dataframe where RESULT is 'Win'
win_df = subset_df[subset_df['RESULT'] == 'Win']

win_df


win_df.describe()


# Remove rows with any missing delta values
win_df = subset_df.dropna(subset=['APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA'])

# Convert 'RESULT' to binary (1 for 'Win', 0 otherwise)
win_df['RESULT_BINARY'] = (win_df['RESULT'] == 'Win').astype(int)

y = win_df['RESULT_BINARY']

# Initialize a dictionary to store the accuracy of each model
accuracy_dict = {}

# Loop through each delta column and perform logistic regression individually
for delta_column in ['APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']:
    # Select the column as the predictor
    X = win_df[[delta_column]]
    
    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Train the model
    logreg = LogisticRegression()
    logreg.fit(X_train, y_train)
    
    # Predict and calculate accuracy
    y_pred = logreg.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    
    # Store the accuracy
    accuracy_dict[delta_column] = accuracy

accuracy_dict

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/1180037860.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  win_df['RESULT_BINARY'] = (win_df['RESULT'] == 'Win').astype(int)

{'APE_INDEX': 0.5348419540229885,
 'HEIGHT_DELTA': 0.5255028735632183,
 'REACH_DELTA': 0.5427442528735632,
 'AGE_DELTA': 0.5520833333333334,
 'FRAME_DELTA': 0.5427442528735632}


# Set up the figure and axes for plotting
fig, axs = plt.subplots(3, 2, figsize=(14, 18))
axs = axs.flatten()  # Flatten the array of axes for easy iteration

# Loop through each delta column and plot
for i, delta_column in enumerate(['APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA',  'FRAME_DELTA']):
    # Prepare the data
    X = win_df[[delta_column]]
    y = win_df['RESULT_BINARY']
    
    # Fit the logistic regression model
    logreg = LogisticRegression()
    logreg.fit(X, y)
    
    # Generate a sequence of values for the predictor variable
    X_test = np.linspace(X.min(), X.max(), 300)
    
    # Predict probabilities for the test sequence
    y_test_pred = logreg.predict_proba(X_test)[:, 1]
    
    # Plot
    axs[i].scatter(X, y, color='blue', alpha=0.2, label='Data Points')
    axs[i].plot(X_test, y_test_pred, color='red', linewidth=2, label='Logistic Regression Curve')
    axs[i].set_xlabel(f'{delta_column}')
    axs[i].set_ylabel('Probability of Win')
    axs[i].set_title(f'Logistic Regression for {delta_column}')
    axs[i].legend()

# Adjust layout and display the plots
plt.tight_layout()
plt.show()

/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names
  warnings.warn(


# Selecting specific columns and dropping duplicates and null values
age_df = master[['BOUT', 'RESULT', 'AGE']].drop_duplicates().dropna()

# Filter where RESULT is either 'Win' or 'Loss'
age_df = age_df[age_df['RESULT'].isin(['Win', 'Loss'])]

# Display 
age_df


age_df.describe()


# Grouping the data by AGE and RESULT and counting occurrences
age_result_count = age_df.groupby(['AGE', 'RESULT']).size().unstack(fill_value=0)

# Plotting
plt.figure(figsize=(14, 7))
for result in age_result_count.columns:
    plt.plot(age_result_count.index, age_result_count[result], label=result)

plt.title('Count of Wins and Losses Across Age')
plt.xlabel('Age')
plt.ylabel('Count')
plt.legend(title='Result')
plt.grid(True)
plt.show()


# Select specific columns and remove duplicates
subset_df = master[['BOUT', 'RESULT', 'APE_INDEX','HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA',  'FRAME_DELTA']].drop_duplicates()

subset_df


win_df = subset_df[subset_df['RESULT'] == 'Win']

win_df


# Creating histograms for the desired columns
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10))
fig.subplots_adjust(hspace=0.5)

columns = ['APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']
for ax, column in zip(axes.flatten(), columns):
    ax.hist(win_df[column].dropna(), bins=20, color='green', alpha=0.7)  # Adjust bin size as necessary
    ax.set_title(f'Histogram of {column}')
    ax.set_xlabel(column)
    ax.set_ylabel('Frequency')

# Remove the empty subplot (if there are an odd number of columns)
fig.delaxes(axes[1][2])

plt.show()


# Create a figure and axis objects
fig, axes = plt.subplots(nrows=len(columns), ncols=1, figsize=(10, 15))

# Plotting histograms
for i, col in enumerate(columns):
    axes[i].hist(win_df[col].dropna(), bins=15, color='green', alpha=0.7)
    axes[i].set_title(f'Histogram of {col}')
    axes[i].set_xlabel(col)
    axes[i].set_ylabel('Frequency')

# Adjust layout
plt.tight_layout()
plt.show()


# Assuming df is your DataFrame
loss_df = subset_df[subset_df['RESULT'] == 'Loss']

loss_df


# Creating histograms for the desired columns
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10))
fig.subplots_adjust(hspace=0.5)

columns = ['APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'AGE_DELTA', 'FRAME_DELTA']
for ax, column in zip(axes.flatten(), columns):
    ax.hist(loss_df[column].dropna(), bins=20, color='red', alpha=0.7)  # Adjust bin size as necessary
    ax.set_title(f'Histogram of {column}')
    ax.set_xlabel(column)
    ax.set_ylabel('Frequency')

# Remove the empty subplot (if there are an odd number of columns)
fig.delaxes(axes[1][2])

plt.show()


# Filtering out 'No Contest' and 'Draw' from the 'RESULT' column
filtered_subset_df = subset_df[~subset_df['RESULT'].isin(['No Contest', 'Draw'])]

# Creating the histograms again with the filtered data
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(14, 18))
axes = axes.flatten()  # Flatten to 1D array for easy iteration

# Plotting histograms for each column, excluding 'No Contest' and 'Draw' results
for i, column in enumerate(columns):
    sns.histplot(data=filtered_subset_df, x=column, hue="RESULT", element="step", stat="density", common_norm=False, ax=axes[i])
    axes[i].set_title(f'Distribution of {column} by Result')
    axes[i].set_xlabel(column)
    axes[i].set_ylabel('Density')

# Remove the last empty subplot (if any)
if len(columns) < len(axes):
    fig.delaxes(axes[-1])

plt.tight_layout()
plt.show()


fb.sort_values(by='Buyrate', ascending=False, inplace = True)
fb.reset_index(drop=True, inplace = True)
# fb.index = range(1, len(fb) + 1)

fb

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/741843745.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  fb.sort_values(by='Buyrate', ascending=False, inplace = True)


fb.describe()


fb.corr()


# Group the data by 'EITHER_CHAMP' and calculate the total buyrate for each category
champ_summary = fb.groupby('EITHER_CHAMP')['Buyrate'].sum().reset_index()

# Calculate the total buyrate to find out the percentage contribution of each category
total_buyrate = champ_summary['Buyrate'].sum()
champ_summary['Percentage'] = (champ_summary['Buyrate'] / total_buyrate) * 100

champ_summary


total_buyrate

2176219652.0


# Group the data by 'EVER_CHALLENGER' and calculate the total buyrate for each category
challenger_summary = fb.groupby('EVER_CHALLENGER')['Buyrate'].sum().reset_index()

# Calculate the total buyrate to find out the percentage contribution of each category
total_buyrate = champ_summary['Buyrate'].sum()
challenger_summary['Percentage'] = (challenger_summary['Buyrate'] / total_buyrate) * 100

challenger_summary


# Group the data by 'STANCE' and calculate the total buyrate for each category
stance_summary = fb.groupby('STANCE')['Buyrate'].sum().reset_index()

# Calculate the total buyrate to find out the percentage contribution of each category
total_buyrate = champ_summary['Buyrate'].sum()
stance_summary['Percentage'] = (stance_summary['Buyrate'] / total_buyrate) * 100

stance_summary


# Correcting the column name and plotting again
plt.figure(figsize=(10, 6))
plt.scatter(fb['PPV_Fights'], fb['Buyrate'], alpha=0.5)
plt.title('Scatter Plot of Buyrate vs. PPV Fights')
plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.grid(True)
plt.show()


# Using seaborn to plot the scatter plot with a regression line
plt.figure(figsize=(10, 6))
sns.regplot(x='PPV_Fights', y='Buyrate', data=fb, scatter_kws={'alpha':0.5})
plt.title('Scatter Plot of Buyrate vs. PPV Fights with Regression Line')
plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.grid(True)
plt.show()


# Clean the data: Remove rows with NaN in either 'Buyrate' or 'PPV_Fights'
df_cleaned = fb.dropna(subset=['Buyrate', 'PPV_Fights'])

# Defining the dependent variable (y) and independent variable (x)
y = df_cleaned['Buyrate'].values.reshape(-1, 1)
x = df_cleaned['PPV_Fights'].values.reshape(-1, 1)

# Linear Regression
model = LinearRegression()
model.fit(x, y)

# Coefficients
slope = model.coef_[0]
intercept = model.intercept_

# Calculate R-squared
r_squared = model.score(x, y)

slope[0], intercept[0], r_squared

(481028.1468489902, 10825.027646517381, 0.8009435134391198)


# Predict y values for the given x values to plot the regression line
y_pred = model.predict(x)

# Plotting
plt.figure(figsize=(10, 6))
plt.scatter(x, y, color='blue', alpha=0.5, label='Actual Buyrate')
plt.plot(x, y_pred, color='red', label='Regression Line')
plt.title('Linear Regression: Buyrate vs PPV_Fights')
plt.xlabel('PPV_Fights')
plt.ylabel('Buyrate')
plt.legend()
plt.grid(True)
plt.show()


# Creating dummy variables for the 'STANCE' column
df_with_dummies = pd.get_dummies(df_cleaned, columns=['STANCE'], drop_first=True)


# Defining the new independent variables including the stance dummies
X_multiple = df_with_dummies[['PPV_Fights', 'STANCE_Southpaw', 'STANCE_Switch']]

# Multiple Linear Regression
model_multiple = LinearRegression()
model_multiple.fit(X_multiple, y)

# Coefficients and Intercept
coefficients = model_multiple.coef_[0]
intercept_multiple = model_multiple.intercept_

# Calculate R-squared for the multiple regression
r_squared_multiple = model_multiple.score(X_multiple, y)

coefficients, intercept_multiple[0], r_squared_multiple

(array([480994.16146437,  33376.03748113, 129287.79240106]),
 -153.882503160974,
 0.8011702621643987)


# Separate data by stance for color-coding
southpaw = df_with_dummies[df_with_dummies['STANCE_Southpaw'] == 1]
switch = df_with_dummies[df_with_dummies['STANCE_Switch'] == 1]
orthodox = df_with_dummies[(df_with_dummies['STANCE_Southpaw'] == 0) & (df_with_dummies['STANCE_Switch'] == 0)]

# Prepare figure
plt.figure(figsize=(10, 6))

# Scatter plot for each stance
plt.scatter(orthodox['PPV_Fights'], orthodox['Buyrate'], color='green', alpha=0.5, label='Orthodox')
plt.scatter(southpaw['PPV_Fights'], southpaw['Buyrate'], color='blue', alpha=0.5, label='Southpaw')
plt.scatter(switch['PPV_Fights'], switch['Buyrate'], color='red', alpha=0.5, label='Switch')

# Add a legend and labels
plt.title('Buyrate vs PPV_Fights by Stance')
plt.xlabel('PPV_Fights')
plt.ylabel('Buyrate')
plt.legend()
plt.grid(True)

plt.show()


# For simplicity, let's calculate and display regression lines for each stance category independently
# This involves fitting a simple linear model for each stance category with respect to PPV_Fights

# Initialize models for each stance
model_orthodox = LinearRegression()
model_southpaw = LinearRegression()
model_switch = LinearRegression()

# Fit the models
model_orthodox.fit(orthodox['PPV_Fights'].values.reshape(-1, 1), orthodox['Buyrate'])
model_southpaw.fit(southpaw['PPV_Fights'].values.reshape(-1, 1), southpaw['Buyrate'])
model_switch.fit(switch['PPV_Fights'].values.reshape(-1, 1), switch['Buyrate'])

# Generate predictions for each stance category for a smooth line
x_range = np.linspace(df_with_dummies['PPV_Fights'].min(), df_with_dummies['PPV_Fights'].max(), 100).reshape(-1, 1)
y_pred_orthodox = model_orthodox.predict(x_range)
y_pred_southpaw = model_southpaw.predict(x_range)
y_pred_switch = model_switch.predict(x_range)

# Plotting
plt.figure(figsize=(10, 6))

# Actual data points
plt.scatter(orthodox['PPV_Fights'], orthodox['Buyrate'], color='green', alpha=0.5, label='Orthodox')
plt.scatter(southpaw['PPV_Fights'], southpaw['Buyrate'], color='blue', alpha=0.5, label='Southpaw')
plt.scatter(switch['PPV_Fights'], switch['Buyrate'], color='red', alpha=0.5, label='Switch')

# Regression lines
plt.plot(x_range, y_pred_orthodox, color='green', linestyle='--', label='Orthodox Regression Line')
plt.plot(x_range, y_pred_southpaw, color='blue', linestyle='--', label='Southpaw Regression Line')
plt.plot(x_range, y_pred_switch, color='red', linestyle='--', label='Switch Regression Line')

plt.title('Buyrate vs PPV_Fights by Stance with Regression Lines')
plt.xlabel('PPV_Fights')
plt.ylabel('Buyrate')
plt.legend()
plt.grid(True)

plt.show()


# Filter out rows where necessary data is missing
df_filtered = fb.dropna(subset=['Buyrate', 'PPV_Fights'])

# Define the features and target variable
X = df_filtered[['EITHER_CHAMP', 'PPV_Fights']]
y = df_filtered['Buyrate']

# Encoding categorical data & Preparing the pipeline for linear regression
categorical_features = ['EITHER_CHAMP']
numerical_features = ['PPV_Fights']

preprocessor = ColumnTransformer(
    transformers=[
        ('num', 'passthrough', numerical_features),
        ('cat', OneHotEncoder(), categorical_features)
    ])

regression_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                                      ('regressor', LinearRegression())])

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Fitting Multiple Linear Regression to the Training set
regression_pipeline.fit(X_train, y_train)

# Predicting the Test set results
y_pred = regression_pipeline.predict(X_test)

# Calculating the performance metrics
r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)

r2, mse

(0.848032301446808, 645916621186.0352)


# Set a larger plot size
plt.figure(figsize=(10, 6))

# Create separate dataframes for 'EITHER_CHAMP' 'Yes' and 'No'
df_champ_yes = df_filtered[df_filtered['EITHER_CHAMP'] == 'Yes']
df_champ_no = df_filtered[df_filtered['EITHER_CHAMP'] == 'No']

# Plotting 'EITHER_CHAMP' 'Yes'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, color='gold', label='EITHER_CHAMP: Yes')

# Plotting regression line for 'EITHER_CHAMP' 'Yes'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, scatter=False, color='gold')

# Plotting 'EITHER_CHAMP' 'No'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, color='silver', label='EITHER_CHAMP: No')

# Plotting regression line for 'EITHER_CHAMP' 'No'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, scatter=False, color='silver')

plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.title('Buyrate vs PPV Fights with Regression Lines for EITHER_CHAMP Yes and No')
plt.legend()
plt.show()


# Filter out rows where necessary data is missing
df_filtered = fb.dropna(subset=['Buyrate', 'PPV_Fights'])

# Define the features and target variable
X = df_filtered[['EVER_CHALLENGER', 'PPV_Fights']]
y = df_filtered['Buyrate']

# Encoding categorical data & Preparing the pipeline for linear regression
categorical_features = ['EVER_CHALLENGER']
numerical_features = ['PPV_Fights']

preprocessor = ColumnTransformer(
    transformers=[
        ('num', 'passthrough', numerical_features),
        ('cat', OneHotEncoder(), categorical_features)
    ])

regression_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                                      ('regressor', LinearRegression())])

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Fitting Multiple Linear Regression to the Training set
regression_pipeline.fit(X_train, y_train)

# Predicting the Test set results
y_pred = regression_pipeline.predict(X_test)

# Calculating the performance metrics
r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)

r2, mse

(0.8597258317738283, 596214969647.7537)


# Set a larger plot size
plt.figure(figsize=(10, 6))

# Create separate dataframes for 'EITHER_CHAMP' 'Yes' and 'No'
df_challenger_yes = df_filtered[df_filtered['EVER_CHALLENGER'] == 'Yes']
df_challenger_no = df_filtered[df_filtered['EVER_CHALLENGER'] == 'No']

# Plotting 'EITHER_CHAMP' 'Yes'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, color='aqua', label='EVER_CHALLENGER: Yes')

# Plotting regression line for 'EITHER_CHAMP' 'Yes'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_yes, scatter=False, color='aqua')

# Plotting 'EITHER_CHAMP' 'No'
sns.scatterplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, color='violet', label='EVER_CHALLENGER: No')

# Plotting regression line for 'EITHER_CHAMP' 'No'
sns.regplot(x='PPV_Fights', y='Buyrate', data=df_champ_no, scatter=False, color='violet')

plt.xlabel('PPV Fights')
plt.ylabel('Buyrate')
plt.title('Buyrate vs PPV Fights with Regression Lines for EVER_CHALLENGER Yes and No')
plt.legend()
plt.show()


# Calculating the total cumulative buyrate
total_cumulative_buyrate = fb['Buyrate'].sum()

total_cumulative_buyrate

2176219652.0


# Calculate mean and median buyrate for each stance
buyrate_stats = fb.groupby('STANCE')['Buyrate'].agg(['mean', 'median'])

# Display the statistics
buyrate_stats


# Prepare data for plotting for the stances
plot_data_stance = buyrate_stats.T  # Transpose to get categories as columns

# Plotting for stances
plot_data_stance.plot(kind='bar', figsize=(10, 6))
plt.title('Mean and Median Buyrate for Each Fighter Stance')
plt.ylabel('Buyrate')
plt.xlabel('Statistic')
plt.xticks(rotation=0)
plt.legend(title='STANCE')
plt.tight_layout()

# Show the plot
plt.show()


# Calculate mean and median ppv fights 
ppv_fight1 = fb.groupby('EITHER_CHAMP')['PPV_Fights'].agg(['mean', 'median'])

# Display the statistics
ppv_fight1


# Calculate mean and median buyrate 
buyrate_stats1 = fb.groupby('EITHER_CHAMP')['Buyrate'].agg(['mean', 'median'])

# Display the statistics
buyrate_stats1


# Calculate mean and median buyrate 
buyrate_percent1 = fb.groupby('EITHER_CHAMP')['Buyrate_PERCENTILE'].agg(['mean', 'median'])

# Display the statistics
buyrate_percent1


# Prepare data for plotting
plot_data = buyrate_stats1.T  # Transpose to get categories as columns

# Plotting
plot_data.plot(kind='bar', figsize=(10, 6))
plt.title('Mean and Median Buyrate for Champion vs Non-champion Fighters')
plt.ylabel('Buyrate')
plt.xlabel('Statistic')
plt.xticks(rotation=0)
plt.legend(title='EITHER_CHAMP')
plt.tight_layout()

# Show the plot
plt.show()


# Calculate mean and median ppv fights 
ppv_fight2 = fb.groupby('EVER_CHALLENGER')['PPV_Fights'].agg(['mean', 'median'])

# Display the statistics
ppv_fight2


# Calculate mean and median buyrate 
buyrate_stats2 = fb.groupby('EVER_CHALLENGER')['Buyrate'].agg(['mean', 'median'])

# Display the statistics
buyrate_stats2


# Calculate mean and median buyrate
buyrate_percent1 = fb.groupby('EVER_CHALLENGER')['Buyrate_PERCENTILE'].agg(['mean', 'median'])

# Display the statistics
buyrate_percent1


# Prepare data for plotting
plot_data = buyrate_stats1.T  # Transpose to get categories as columns

# Plotting
plot_data.plot(kind='bar', figsize=(10, 6))
plt.title('Mean and Median Buyrate for Challenger vs Non-challenger Fighters')
plt.ylabel('Buyrate')
plt.xlabel('Statistic')
plt.xticks(rotation=0)
plt.legend(title='EVER_CHALLENGER')
plt.tight_layout()

# Show the plot
plt.show()


# Calculate the number of entries that represent the top 10% of the dataset
top_10_percent_count = int(len(fb) * 0.1)

# Sort the dataset by 'Buyrate' descending and take the top 10%
top_10_percent_earners = fb.sort_values(by='Buyrate', ascending=False).head(top_10_percent_count)

top_10_percent_earners


# Calculate the number of entries that represent the top 1% of the dataset
top_1_percent_count = int(len(fb) * 0.01)

# Sort the dataset by 'Buyrate' descending and take the top 10%
top_1_percent_earners = fb.sort_values(by='Buyrate', ascending=False).head(top_1_percent_count)

top_1_percent_earners


# Filter to top 1% fighters by their buyrate. 
top = top_1_percent_earners

# Visualization with vertical bars
plt.figure(figsize=(12, 7))
plt.bar(top['FIGHTER'], top['Buyrate'], color='lightgreen')
plt.xticks(rotation=45, ha='right')
plt.ylabel('Buyrate')
plt.title('Top 1% Fighters by Buyrate')
plt.show()


top_1_percent_list

['Conor McGregor',
 'Georges St-Pierre',
 'Jim Miller',
 'Jon Jones',
 'Demian Maia',
 'Anderson Silva',
 'Johny Hendricks',
 'Frank Mir',
 'Donald Cerrone',
 'Diego Sanchez',
 'Jeremy Stephens',
 'Dustin Poirier',
 'Jon Fitch']


top_10_percent_list

['Conor McGregor',
 'Georges St-Pierre',
 'Jim Miller',
 'Jon Jones',
 'Demian Maia',
 'Anderson Silva',
 'Johny Hendricks',
 'Frank Mir',
 'Donald Cerrone',
 'Diego Sanchez',
 'Jeremy Stephens',
 'Dustin Poirier',
 'Jon Fitch',
 'Frankie Edgar',
 'Dong Hyun Kim',
 'Nate Diaz',
 'Thiago Alves',
 'Brock Lesnar',
 'Michael Bisping',
 'Tim Boetsch',
 'Khabib Nurmagomedov',
 'Gleison Tibau',
 'Rashad Evans',
 'Lyoto Machida',
 'Mark Bocek',
 'Forrest Griffin',
 'Matt Brown',
 'Tito Ortiz',
 'Anthony Pettis',
 'Gabriel Gonzaga',
 'Stefan Struve',
 'BJ Penn',
 'Chris Weidman',
 'John Makdessi',
 'Patrick Cote',
 'Cody Garbrandt',
 'Rick Story',
 'Josh Koscheck',
 'Matt Hughes',
 'Nik Lentz',
 'Alan Belcher',
 'Stephen Thompson',
 'Carlos Condit',
 'Cheick Kongo',
 'Chris Leben',
 'Jose Aldo',
 'Tony Ferguson',
 'Chuck Liddell',
 'Melvin Guillard',
 'Amanda Nunes',
 'Ronda Rousey',
 'Nate Marquardt',
 'Yushin Okami',
 'Daniel Cormier',
 'Stephan Bonnar',
 'Cain Velasquez',
 'Quinton Jackson',
 'Raquel Pennington',
 'Sam Stout',
 'Max Holloway',
 'Tyron Woodley',
 'Rich Franklin',
 'Jason MacDonald',
 'Tom Lawlor',
 'Randy Couture',
 'Brad Tavares',
 'Joe Lauzon',
 'Dan Miller',
 'Ovince Saint Preux',
 'TJ Grant',
 'Chris Lytle',
 'Miesha Tate',
 'Kurt Pellegrino',
 'Neil Magny',
 'Andrei Arlovski',
 'Robbie Lawler',
 'Holly Holm',
 'Jessica Eye',
 'Joe Stevenson',
 'Mauricio Rua',
 'Chael Sonnen',
 'Brendan Schaub',
 'Kenny Florian',
 'Travis Browne',
 'Rose Namajunas',
 'Mike Pyle',
 'Antonio Rodrigo Nogueira',
 'Uriah Hall',
 'Martin Kampmann',
 'Drew Dober',
 'Sergio Pettis',
 'Ricardo Almeida',
 'Kendall Grove',
 'Urijah Faber',
 'Thales Leites',
 'Gian Villante',
 'Edson Barboza',
 'John Howard',
 'Zhalgas Zhumagulov',
 'Tyson Griffin',
 'Nick Diaz',
 'TJ Dillashaw',
 'Junior Dos Santos',
 'Darren Elkins',
 'Mark Hunt',
 'Joanna Jedrzejczyk',
 'Matt Hamill',
 'Gray Maynard',
 'Rafael Dos Anjos',
 'Dan Henderson',
 'Yana Kunitskaya',
 'Mac Danzig',
 'CB Dollaway',
 'Vicente Luque',
 'Clay Guida',
 'Anthony Johnson',
 'Ed Herman',
 'Rory MacDonald',
 'Colby Covington',
 'Tim Means',
 'Manvel Gamburyan',
 'Chad Mendes',
 'Rafael Natal',
 'Paulo Thiago',
 'Thiago Santos',
 'Luiz Cane',
 'Joshua Burkman',
 'Evan Dunham',
 'George Sotiropoulos',
 'Ryan Jensen',
 'Jake Ellenberger',
 'Omari Akhmedov',
 'Keith Jardine',
 'Dominick Cruz',
 'Krzysztof Soszynski',
 'Max Griffin',
 'Derrick Lewis',
 'Aaron Riley']


# For the top 10% earners, we'll calculate the numbers and percentages for the 'STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', and 'EVER_CHALLENGER' columns

# Filtering to the top 10% earners
top_10_percent = fb_sorted_by_buyrate.head(top_10_percent_count)

# Calculating counts and percentages for each specified column
columns_of_interest = ['STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', 'EVER_CHALLENGER', 'EITHER_CHAMP']
summary = {}

for column in columns_of_interest:
    counts = top_10_percent[column].value_counts()
    percentages = top_10_percent[column].value_counts(normalize=True) * 100
    summary[column] = pd.DataFrame({'Number': counts, 'Percentage': percentages})

summary

{'STANCE':           Number  Percentage
 Orthodox     107   77.536232
 Southpaw      28   20.289855
 Switch         3    2.173913,
 'EVER_CHAMPION':      Number  Percentage
 No       96   69.565217
 Yes      42   30.434783,
 'EVER_INTERIM':      Number  Percentage
 No      125    90.57971
 Yes      13     9.42029,
 'EVER_CHALLENGER':      Number  Percentage
 Yes      76   55.072464
 No       62   44.927536,
 'EITHER_CHAMP':      Number  Percentage
 No       91   65.942029
 Yes      47   34.057971}


# For the top 1% earners, we'll calculate the numbers and percentages for the 'STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', and 'EVER_CHALLENGER' columns

# Filtering to the top 1% earners
top_1_percent = fb_sorted_by_buyrate.head(top_1_percent_count)

# Calculating counts and percentages for each specified column
columns_of_interest = ['STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', 'EVER_CHALLENGER', 'EITHER_CHAMP']
summary = {}

for column in columns_of_interest:
    counts = top_1_percent[column].value_counts()
    percentages = top_1_percent[column].value_counts(normalize=True) * 100
    summary[column] = pd.DataFrame({'Number': counts, 'Percentage': percentages})

summary

{'STANCE':           Number  Percentage
 Southpaw       8   61.538462
 Orthodox       5   38.461538,
 'EVER_CHAMPION':      Number  Percentage
 No        7   53.846154
 Yes       6   46.153846,
 'EVER_INTERIM':      Number  Percentage
 No        8   61.538462
 Yes       5   38.461538,
 'EVER_CHALLENGER':      Number  Percentage
 Yes      11   84.615385
 No        2   15.384615,
 'EITHER_CHAMP':      Number  Percentage
 Yes       7   53.846154
 No        6   46.153846}


# Remove rows where either 'STANCE' or 'Buyrate' is missing
cleaned_data = fb.dropna(subset=['STANCE', 'Buyrate'])

# Plotting
plt.figure(figsize=(10, 6))
sns.boxplot(x='STANCE', y='Buyrate', data=cleaned_data)
plt.title('Buyrate Distribution by Stance')
plt.xlabel('Stance')
plt.ylabel('Buyrate')
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()

plt.show()


# Prepare data for ANOVA
stances = cleaned_data['STANCE'].unique()
grouped_data = [cleaned_data['Buyrate'][cleaned_data['STANCE'] == stance] for stance in stances]

# Perform ANOVA
anova_result = f_oneway(*grouped_data)

anova_result

F_onewayResult(statistic=2.0003919810677537, pvalue=0.13567748040766384)


# Perform Tukey HSD test
tukey_result = pairwise_tukeyhsd(endog=cleaned_data['Buyrate'], groups=cleaned_data['STANCE'], alpha=0.05)

# Convert the result to a DataFrame for better readability
tukey_summary = pd.DataFrame(data=tukey_result.summary().data[1:], columns=tukey_result.summary().data[0])

tukey_summary


# First, let's convert the 'EITHER_CHAMP' column to a binary format where 'Yes' is 1 and 'No' is 0
fb['EITHER_CHAMP_BINARY'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)

# Now, let's calculate the correlation between 'EITHER_CHAMP_BINARY' and 'Buyrate'
correlation = fb[['EITHER_CHAMP_BINARY', 'Buyrate']].corr()

correlation

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3371666952.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  fb['EITHER_CHAMP_BINARY'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)


# First, let's convert the 'EITHER_CHAMP' column to a binary format where 'Yes' is 1 and 'No' is 0
fb['EVER_CHALLENGER_BINARY'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)

# Now, let's calculate the correlation between 'EITHER_CHAMP_BINARY' and 'Buyrate'
correlation = fb[['EVER_CHALLENGER_BINARY', 'Buyrate']].corr()

correlation

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/1082348933.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  fb['EVER_CHALLENGER_BINARY'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)


# Create dummy variables for EITHER_CHAMP and EVER_CHALLENGER columns
fb['EITHER_CHAMP_dummy'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)
fb['EVER_CHALLENGER_dummy'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)

# Create dummy variables for each value in the STANCE column
stance_dummies = pd.get_dummies(fb['STANCE'], prefix='STANCE')

# Combine the dummy variables with the original dataframe
fb_combined = pd.concat([fb, stance_dummies], axis=1)

# Display the columns of the modified dataframe to ensure the dummies were added correctly
# fb_combined.columns.tolist()

# Correcting the mistake to include STANCE dummy variables in the correlation matrix
full_correlation_matrix = fb_combined[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights', 
                                       'EITHER_CHAMP_dummy', 'EVER_CHALLENGER_dummy', 
                                       'STANCE_Orthodox', 'STANCE_Southpaw', 'STANCE_Switch']].corr()

full_correlation_matrix

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3780777172.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  fb['EITHER_CHAMP_dummy'] = (fb['EITHER_CHAMP'] == 'Yes').astype(int)
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3780777172.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  fb['EVER_CHALLENGER_dummy'] = (fb['EVER_CHALLENGER'] == 'Yes').astype(int)


# Create dummy variables for EITHER_CHAMP and EVER_CHALLENGER columns
top_10_percent['EITHER_CHAMP_dummy'] = (top_10_percent['EITHER_CHAMP'] == 'Yes').astype(int)
top_10_percent['EVER_CHALLENGER_dummy'] = (top_10_percent['EVER_CHALLENGER'] == 'Yes').astype(int)

# Create dummy variables for each value in the STANCE column
stance_dummies = pd.get_dummies(top_10_percent['STANCE'], prefix='STANCE')

# Combine the dummy variables with the original dataframe
fb_combined = pd.concat([top_10_percent, stance_dummies], axis=1)

# Display the columns of the modified dataframe to ensure the dummies were added correctly
# fb_combined.columns.tolist()

# Correcting the mistake to include STANCE dummy variables in the correlation matrix
full_correlation_matrix = fb_combined[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights', 
                                       'EITHER_CHAMP_dummy', 'EVER_CHALLENGER_dummy', 
                                       'STANCE_Orthodox', 'STANCE_Southpaw', 'STANCE_Switch']].corr()

full_correlation_matrix

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/2837436102.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  top_10_percent['EITHER_CHAMP_dummy'] = (top_10_percent['EITHER_CHAMP'] == 'Yes').astype(int)
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/2837436102.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  top_10_percent['EVER_CHALLENGER_dummy'] = (top_10_percent['EVER_CHALLENGER'] == 'Yes').astype(int)


# Create dummy variables for EITHER_CHAMP and EVER_CHALLENGER columns
top_1_percent['EITHER_CHAMP_dummy'] = (top_1_percent['EITHER_CHAMP'] == 'Yes').astype(int)
top_1_percent['EVER_CHALLENGER_dummy'] = (top_1_percent['EVER_CHALLENGER'] == 'Yes').astype(int)

# Create dummy variables for each value in the STANCE column
stance_dummies = pd.get_dummies(top_1_percent['STANCE'], prefix='STANCE')

# Combine the dummy variables with the original dataframe
fb_combined = pd.concat([top_1_percent, stance_dummies], axis=1)

# Display the columns of the modified dataframe to ensure the dummies were added correctly
# fb_combined.columns.tolist()

# Correcting the mistake to include STANCE dummy variables in the correlation matrix
full_correlation_matrix = fb_combined[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights', 
                                       'EITHER_CHAMP_dummy', 'EVER_CHALLENGER_dummy', 
                                       'STANCE_Orthodox', 'STANCE_Southpaw']].corr()

full_correlation_matrix

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/860985673.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  top_1_percent['EITHER_CHAMP_dummy'] = (top_1_percent['EITHER_CHAMP'] == 'Yes').astype(int)
/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/860985673.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  top_1_percent['EVER_CHALLENGER_dummy'] = (top_1_percent['EVER_CHALLENGER'] == 'Yes').astype(int)


# Display max rows
pd.set_option('display.max_rows', None)

breakdown(frame)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Distinct Count 
 FIGHTER                    2317
STANCE                        3
EVER_CHAMPION                 2
EVER_INTERIM                  2
EVER_CHALLENGER               2
EITHER_CHAMP                  2
HEIGHT                       23
WEIGHT                       48
REACH                        27
APE_INDEX                   179
FRAME                        44
HEIGHT_DIFF                 772
REACH_DIFF                  809
AGE_DIFF                   1583
AI_DIFF                    1078
FRAME_DIFF                  906
OPP_HEIGHT                  569
OPP_WEIGHT                  618
OPP_REACH                   592
OPP_APE_INDEX              1829
OPP_FRAME                   748
OPP_REACH_DIFF             1431
OPP_AGE_DIFF               1597
OPP_AI_DIFF                2052
OPP_FRAME_DIFF             1534
HEIGHT_DELTA                467
REACH_DELTA                 518
AGE_DELTA                   610
AI_DELTA                   1916
FRAME_DELTA                 620
HEIGHT_DELTA_RANK           467
REACH_DELTA_RANK            518
AGE_DELTA_RANK              610
AI_DELTA_RANK              1916
FRAME_DELTA_RANK            620
HEIGHT_DELTA_PERCENTILE     467
REACH_DELTA_PERCENTILE      518
FRAME_DELTA_PERCENTILE      620
dtype: int64


 Count 
 FIGHTER                    2317
STANCE                     2075
EVER_CHAMPION              2317
EVER_INTERIM               2317
EVER_CHALLENGER            2317
EITHER_CHAMP               2317
HEIGHT                     2180
WEIGHT                     2285
REACH                      2180
APE_INDEX                  2180
FRAME                      2180
HEIGHT_DIFF                2180
REACH_DIFF                 2180
AGE_DIFF                   2221
AI_DIFF                    2180
FRAME_DIFF                 2180
OPP_HEIGHT                 2278
OPP_WEIGHT                 2310
OPP_REACH                  2278
OPP_APE_INDEX              2278
OPP_FRAME                  2278
OPP_REACH_DIFF             2278
OPP_AGE_DIFF               2297
OPP_AI_DIFF                2278
OPP_FRAME_DIFF             2278
HEIGHT_DELTA               2173
REACH_DELTA                2173
AGE_DELTA                  2206
AI_DELTA                   2173
FRAME_DELTA                2173
HEIGHT_DELTA_RANK          2173
REACH_DELTA_RANK           2173
AGE_DELTA_RANK             2206
AI_DELTA_RANK              2173
FRAME_DELTA_RANK           2173
HEIGHT_DELTA_PERCENTILE    2173
REACH_DELTA_PERCENTILE     2173
FRAME_DELTA_PERCENTILE     2173
dtype: int64


 DTypes 
 FIGHTER                     object
STANCE                      object
EVER_CHAMPION               object
EVER_INTERIM                object
EVER_CHALLENGER             object
EITHER_CHAMP                object
HEIGHT                     float64
WEIGHT                     float64
REACH                      float64
APE_INDEX                  float64
FRAME                      float64
HEIGHT_DIFF                float64
REACH_DIFF                 float64
AGE_DIFF                   float64
AI_DIFF                    float64
FRAME_DIFF                 float64
OPP_HEIGHT                 float64
OPP_WEIGHT                 float64
OPP_REACH                  float64
OPP_APE_INDEX              float64
OPP_FRAME                  float64
OPP_REACH_DIFF             float64
OPP_AGE_DIFF               float64
OPP_AI_DIFF                float64
OPP_FRAME_DIFF             float64
HEIGHT_DELTA               float64
REACH_DELTA                float64
AGE_DELTA                  float64
AI_DELTA                   float64
FRAME_DELTA                float64
HEIGHT_DELTA_RANK          float64
REACH_DELTA_RANK           float64
AGE_DELTA_RANK             float64
AI_DELTA_RANK              float64
FRAME_DELTA_RANK           float64
HEIGHT_DELTA_PERCENTILE    float64
REACH_DELTA_PERCENTILE     float64
FRAME_DELTA_PERCENTILE     float64
dtype: object


 Shape 
 (2317, 38)


 Nulls 
 FIGHTER                      0
STANCE                     242
EVER_CHAMPION                0
EVER_INTERIM                 0
EVER_CHALLENGER              0
EITHER_CHAMP                 0
HEIGHT                     137
WEIGHT                      32
REACH                      137
APE_INDEX                  137
FRAME                      137
HEIGHT_DIFF                137
REACH_DIFF                 137
AGE_DIFF                    96
AI_DIFF                    137
FRAME_DIFF                 137
OPP_HEIGHT                  39
OPP_WEIGHT                   7
OPP_REACH                   39
OPP_APE_INDEX               39
OPP_FRAME                   39
OPP_REACH_DIFF              39
OPP_AGE_DIFF                20
OPP_AI_DIFF                 39
OPP_FRAME_DIFF              39
HEIGHT_DELTA               144
REACH_DELTA                144
AGE_DELTA                  111
AI_DELTA                   144
FRAME_DELTA                144
HEIGHT_DELTA_RANK          144
REACH_DELTA_RANK           144
AGE_DELTA_RANK             111
AI_DELTA_RANK              144
FRAME_DELTA_RANK           144
HEIGHT_DELTA_PERCENTILE    144
REACH_DELTA_PERCENTILE     144
FRAME_DELTA_PERCENTILE     144
dtype: int64


 Null % 
 FIGHTER                     0.00
STANCE                     10.44
EVER_CHAMPION               0.00
EVER_INTERIM                0.00
EVER_CHALLENGER             0.00
EITHER_CHAMP                0.00
HEIGHT                      5.91
WEIGHT                      1.38
REACH                       5.91
APE_INDEX                   5.91
FRAME                       5.91
HEIGHT_DIFF                 5.91
REACH_DIFF                  5.91
AGE_DIFF                    4.14
AI_DIFF                     5.91
FRAME_DIFF                  5.91
OPP_HEIGHT                  1.68
OPP_WEIGHT                  0.30
OPP_REACH                   1.68
OPP_APE_INDEX               1.68
OPP_FRAME                   1.68
OPP_REACH_DIFF              1.68
OPP_AGE_DIFF                0.86
OPP_AI_DIFF                 1.68
OPP_FRAME_DIFF              1.68
HEIGHT_DELTA                6.21
REACH_DELTA                 6.21
AGE_DELTA                   4.79
AI_DELTA                    6.21
FRAME_DELTA                 6.21
HEIGHT_DELTA_RANK           6.21
REACH_DELTA_RANK            6.21
AGE_DELTA_RANK              4.79
AI_DELTA_RANK               6.21
FRAME_DELTA_RANK            6.21
HEIGHT_DELTA_PERCENTILE     6.21
REACH_DELTA_PERCENTILE      6.21
FRAME_DELTA_PERCENTILE      6.21
dtype: float64


frame.describe()


subset_columns = [
    'FIGHTER', 'STANCE', 'EVER_CHAMPION', 'EVER_INTERIM', 'EVER_CHALLENGER', 'EITHER_CHAMP', 
    'APE_INDEX', 'HEIGHT_DELTA', 'REACH_DELTA', 'FRAME_DELTA', 'HEIGHT_DELTA_PERCENTILE',
    'REACH_DELTA_PERCENTILE', 'FRAME_DELTA_PERCENTILE'
]

# Creating the subset dataframe
subset_df = frame[subset_columns]


frame_champions = subset_df[subset_df['EITHER_CHAMP'] == 'Yes']

frame_champions


frame_champions.describe()


frame_challengers = subset_df[subset_df['EVER_CHALLENGER'] == 'Yes']

frame_challengers


frame_challengers.describe()


frame_non_champions = subset_df[subset_df['EITHER_CHAMP'] != 'Yes']

frame_non_champions.describe()


frame_non_challengers = subset_df[subset_df['EVER_CHALLENGER'] != 'Yes']

frame_non_challengers.describe()


# Plotting the histogram of APE_INDEX for fighters who have been a champion
plt.figure(figsize=(10, 6))
plt.hist(frame_champions['APE_INDEX'].dropna(), bins=20, color='gold', edgecolor='black')
plt.title('APE_Index of Champions')
plt.xlabel('APE_INDEX')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)

# Show the plot
plt.show()


# Separate APE_INDEX based on EITHER_CHAMP 'Yes' or 'No'
ape_yes = frame[frame['EITHER_CHAMP'] == 'Yes']['APE_INDEX']
ape_no = frame[frame['EITHER_CHAMP'] == 'No']['APE_INDEX']

# Plotting the histogram with two colors
plt.figure(figsize=(10, 6))
plt.hist([ape_yes.dropna(), ape_no.dropna()], bins=20, color=['gold', 'silver'], label=['Champion', 'Non-Champion'], edgecolor='black')
plt.title('Histogram of APE_INDEX for All Fighters')
plt.xlabel('APE_INDEX')
plt.ylabel('Frequency')
plt.legend()
plt.grid(axis='y', alpha=0.75)

# Show the plot
plt.show()


# Plotting the histogram of FRAME_DELTA for fighters who have been a champion
plt.figure(figsize=(10, 6))
plt.hist(frame_champions['FRAME_DELTA'].dropna(), bins=20, color='chocolate', edgecolor='black')
plt.title('FRAME_DELTA of Champions')
plt.xlabel('FRAME_DELTA')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)

# Show the plot
plt.show()


# Separate APE_INDEX based on EITHER_CHAMP 'Yes' or 'No'
h_yes = frame[frame['EITHER_CHAMP'] == 'Yes']['FRAME_DELTA']
h_no = frame[frame['EITHER_CHAMP'] == 'No']['FRAME_DELTA']

# Plotting the histogram with two colors
plt.figure(figsize=(10, 6))
plt.hist([h_yes.dropna(), h_no.dropna()], bins=20, color=['chocolate', 'lawngreen'], label=['Champion', 'Non-Champion'], edgecolor='black')
plt.title('Histogram of FRAME_DELTA for All Fighters')
plt.xlabel('FRAME_DELTA')
plt.ylabel('Frequency')
plt.legend()
plt.grid(axis='y', alpha=0.75)

# Show the plot
plt.show()


# Filter the dataframe using the correct column name "FIGHTER"
c = career[career['FIGHTER'].isin(fighters_list)]

c


c.describe()


# Selecting general performance indicators based on available columns
metrics = [
    "KD", "TD", "SUB.ATT", "REV", "CTRL", "HEAD", "BODY", "LEG", "DISTANCE",
    "CLINCH", "GROUND", "SIG", "STR", "TD_DEF", "HEAD_DEF", "BODY_DEF", "LEG_DEF",
    "DISTANCE_DEF", "CLINCH_DEF", "GROUND_DEF", "SIG_DEF", "STR_DEF", "TD_%",
    "HEAD_%", "BODY_%", "LEG_%", "DISTANCE_%", "CLINCH_%", "GROUND_%", "SIG_%",
    "STR_%", "TD_DEF_%", "HEAD_DEF_%", "BODY_DEF_%", "LEG_DEF_%", "DISTANCE_DEF_%",
    "CLINCH_DEF_%", "GROUND_DEF_%", "SIG_DEF_%", "STR_DEF_%"
]

# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}

# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}

# Finding the top ten fighters for each metric
for metric in metrics:
    # Some metrics may have missing values, so we sort only non-NA values
    top_fighters_per_metric[metric] = c.nlargest(10, metric)[['FIGHTER', metric]]

# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
    print(f"Top ten fighters for {metric}:")
    if isinstance(data, pd.DataFrame):
        print(data.to_string(index=False))
    else:
        print(data)
    print("\n")  # Blank line for separation

Top ten fighters for KD:
          FIGHTER   KD
   Donald Cerrone 20.0
  Jeremy Stephens 18.0
   Anderson Silva 18.0
    Edson Barboza 16.0
   Dustin Poirier 14.0
     Mauricio Rua 14.0
    Thiago Santos 14.0
Junior Dos Santos 14.0
    Lyoto Machida 14.0
    Chuck Liddell 14.0


Top ten fighters for TD:
           FIGHTER   TD
 Georges St-Pierre 90.0
     Gleison Tibau 84.0
 Merab Dvalishvili 79.0
        Clay Guida 78.0
Demetrious Johnson 74.0
     Frankie Edgar 73.0
   Colby Covington 69.0
         Nik Lentz 69.0
  Rafael Dos Anjos 68.0
       Demian Maia 68.0


Top ten fighters for SUB.ATT:
         FIGHTER  SUB.ATT
      Jim Miller     48.0
Charles Oliveira     40.0
     Chris Lytle     31.0
      Joe Lauzon     29.0
     Demian Maia     27.0
       Nate Diaz     26.0
       Nik Lentz     26.0
   Darren Elkins     25.0
  Dustin Poirier     24.0
      Matt Brown     24.0


Top ten fighters for REV:
        FIGHTER  REV
   Louis Smolka 13.0
   Alex Caceres 12.0
 Anthony Pettis 11.0
   Dustin Ortiz 11.0
  Brendan Allen  9.0
  Carlos Condit  9.0
    Cole Miller  9.0
    Chris Lytle  9.0
     Paul Kelly  9.0
Kelvin Gastelum  8.0


Top ten fighters for CTRL:
          FIGHTER       CTRL
Georges St-Pierre 162.116667
       Clay Guida 156.500000
      Demian Maia 155.266667
 Rafael Dos Anjos 136.750000
     Kamaru Usman 133.983333
        Jon Fitch 128.383333
    Randy Couture 126.766667
    Darren Elkins 121.766667
  Colby Covington 121.550000
      Matt Hughes 117.800000


Top ten fighters for HEAD:
         FIGHTER   HEAD
    Max Holloway 2117.0
 Sean Strickland 1558.0
  Dustin Poirier 1343.0
   Frankie Edgar 1195.0
       Nate Diaz 1146.0
     Angela Hill 1136.0
 Michael Bisping 1132.0
Rafael Dos Anjos 1061.0
     Evan Dunham 1039.0
 Jessica Andrade 1021.0


Top ten fighters for BODY:
           FIGHTER  BODY
      Max Holloway 723.0
       Angela Hill 594.0
   Michael Johnson 451.0
     Edson Barboza 449.0
    Donald Cerrone 442.0
Joanna Jedrzejczyk 435.0
  Rafael Dos Anjos 433.0
       Bobby Green 412.0
         Tim Means 402.0
 Aljamain Sterling 390.0


Top ten fighters for LEG:
              FIGHTER   LEG
   Joanna Jedrzejczyk 508.0
          Joanne Wood 425.0
            Jon Jones 420.0
       Donald Cerrone 413.0
Alexander Volkanovski 407.0
         Pedro Munhoz 405.0
      Chris Gutierrez 389.0
         Thiago Alves 387.0
      Israel Adesanya 363.0
      Andrei Arlovski 357.0


Top ten fighters for DISTANCE:
           FIGHTER  DISTANCE
      Max Holloway    2709.0
   Sean Strickland    1712.0
    Donald Cerrone    1478.0
Joanna Jedrzejczyk    1431.0
       Angela Hill    1380.0
       Bobby Green    1353.0
     Edson Barboza    1303.0
   Andrei Arlovski    1298.0
   Michael Johnson    1285.0
   Michael Bisping    1271.0


Top ten fighters for CLINCH:
           FIGHTER  CLINCH
       Angela Hill   453.0
Demetrious Johnson   359.0
 Raquel Pennington   354.0
         Nate Diaz   342.0
  Rafael Dos Anjos   321.0
Joanna Jedrzejczyk   307.0
   Jessica Andrade   296.0
     Robbie Lawler   288.0
        Matt Brown   276.0
        Neil Magny   263.0


Top ten fighters for GROUND:
            FIGHTER  GROUND
  Georges St-Pierre   461.0
      Frankie Edgar   409.0
Khabib Nurmagomedov   380.0
    Glover Teixeira   373.0
       Kamaru Usman   345.0
      Diego Sanchez   334.0
         Neil Magny   332.0
          Jon Fitch   308.0
      Randy Couture   289.0
        Demian Maia   279.0


Top ten fighters for SIG:
           FIGHTER    SIG
      Max Holloway 3122.0
       Angela Hill 1896.0
   Sean Strickland 1887.0
  Rafael Dos Anjos 1818.0
     Frankie Edgar 1801.0
Joanna Jedrzejczyk 1754.0
    Donald Cerrone 1748.0
    Dustin Poirier 1678.0
       Bobby Green 1668.0
   Jessica Andrade 1591.0


Top ten fighters for STR:
          FIGHTER    STR
     Max Holloway 3366.0
Georges St-Pierre 2591.0
 Rafael Dos Anjos 2587.0
       Neil Magny 2525.0
    Darren Elkins 2501.0
        Nate Diaz 2487.0
    Frankie Edgar 2481.0
     Kamaru Usman 2369.0
      Angela Hill 2200.0
        Jon Fitch 2185.0


Top ten fighters for TD_DEF:
           FIGHTER  TD_DEF
      Max Holloway   126.0
          Petr Yan   109.0
Joanna Jedrzejczyk   109.0
           BJ Penn   109.0
         Sam Stout   102.0
      Brad Tavares    96.0
  Rafael Dos Anjos    91.0
   Michael Johnson    86.0
         Jose Aldo    86.0
       Angela Hill    85.0


Top ten fighters for HEAD_DEF:
         FIGHTER  HEAD_DEF
    Max Holloway    2695.0
   Frankie Edgar    2452.0
     Angela Hill    2426.0
Rafael Dos Anjos    2348.0
   Robbie Lawler    2106.0
      Clay Guida    1967.0
 Sean Strickland    1937.0
   John Makdessi    1925.0
    Ross Pearson    1853.0
   Edson Barboza    1852.0


Top ten fighters for BODY_DEF:
              FIGHTER  BODY_DEF
      Sean Strickland     316.0
          Angela Hill     258.0
         Max Holloway     245.0
       Donald Cerrone     224.0
         Pedro Munhoz     181.0
     Rafael Dos Anjos     178.0
          Bobby Green     167.0
          John Dodson     164.0
Karolina Kowalkiewicz     161.0
    Raquel Pennington     160.0


Top ten fighters for LEG_DEF:
             FIGHTER  LEG_DEF
Valentina Shevchenko    128.0
        Max Holloway    125.0
     Sean Strickland    102.0
           Jose Aldo     91.0
       Lyoto Machida     89.0
  Joanna Jedrzejczyk     88.0
           Jon Jones     86.0
         Bobby Green     82.0
        Dennis Siver     79.0
   Katlyn Chookagian     76.0


Top ten fighters for DISTANCE_DEF:
         FIGHTER  DISTANCE_DEF
    Max Holloway        2964.0
     Angela Hill        2557.0
   Frankie Edgar        2501.0
Rafael Dos Anjos        2405.0
 Sean Strickland        2298.0
   Robbie Lawler        2148.0
   John Makdessi        2108.0
      Clay Guida        1978.0
    Ross Pearson        1907.0
   Edson Barboza        1905.0


Top ten fighters for CLINCH_DEF:
          FIGHTER  CLINCH_DEF
      Angela Hill       162.0
    Robbie Lawler       144.0
        Jon Jones       134.0
     Rashad Evans       121.0
        Nick Diaz       113.0
          BJ Penn       110.0
Raquel Pennington       108.0
  Jessica Andrade       108.0
       Joe Lauzon       108.0
        Nate Diaz       107.0


Top ten fighters for GROUND_DEF:
             FIGHTER  GROUND_DEF
           Nate Diaz       163.0
       Elvis Sinosic       144.0
       Hermes Franca       142.0
          Tito Ortiz       139.0
       Diego Sanchez       131.0
      Iuri Alcantara       127.0
      Spencer Fisher       125.0
Germaine de Randamie       118.0
      Anderson Silva       117.0
             BJ Penn       114.0


Top ten fighters for SIG_DEF:
         FIGHTER  SIG_DEF
    Max Holloway   3065.0
     Angela Hill   2743.0
   Frankie Edgar   2605.0
Rafael Dos Anjos   2551.0
 Sean Strickland   2355.0
   Robbie Lawler   2307.0
   John Makdessi   2146.0
      Clay Guida   2085.0
    Ross Pearson   2037.0
   Edson Barboza   2032.0


Top ten fighters for STR_DEF:
         FIGHTER  STR_DEF
    Max Holloway   3085.0
     Angela Hill   2845.0
Rafael Dos Anjos   2650.0
   Frankie Edgar   2634.0
 Sean Strickland   2377.0
   Robbie Lawler   2331.0
      Clay Guida   2157.0
   John Makdessi   2151.0
   Edson Barboza   2113.0
    Ross Pearson   2067.0


Top ten fighters for TD_%:
         FIGHTER  TD_%
   Johnny Walker   1.0
      Sumudaerji   1.0
    Jared Gooden   1.0
      Uros Medic   1.0
    Alex Pereira   1.0
    Tom Aspinall   1.0
    Andre Fialho   1.0
    Omar Morales   1.0
Cheyanne Vlismas   1.0
  Mariya Agapova   1.0


Top ten fighters for HEAD_%:
          FIGHTER   HEAD_%
   Danilo Marques 0.769231
Umar Nurmagomedov 0.681633
  Jailton Almeida 0.662037
    Tatsuro Taira 0.659898
  Cyrille Diabate 0.658228
     Brock Lesnar 0.652361
 Alistair Overeem 0.650927
   Manny Bermudez 0.590164
   Jerrod Sanders 0.586207
     Tom Aspinall 0.583333


Top ten fighters for BODY_%:
          FIGHTER  BODY_%
   Jeremy Kimball     1.0
   Jerrod Sanders     1.0
        Paul Sass     1.0
        Ken Stone     1.0
     Shane Carwin     1.0
Chris Tuchscherer     1.0
     Jake O'Brien     1.0
   Alvin Robinson     1.0
      Rory Singer     1.0
      Marcio Cruz     1.0


Top ten fighters for LEG_%:
         FIGHTER  LEG_%
   Nikolas Motta    1.0
    Martin Buday    1.0
    Steve Garcia    1.0
     Joe Solecki    1.0
      Uros Medic    1.0
Sergei Pavlovich    1.0
    Tom Aspinall    1.0
     Nate Maness    1.0
   Jacob Malkoun    1.0
     Zarah Fairn    1.0


Top ten fighters for DISTANCE_%:
          FIGHTER  DISTANCE_%
  Cyrille Diabate    0.680769
   Danilo Marques    0.675000
 Alistair Overeem    0.651452
    Tatsuro Taira    0.630000
     Tom Aspinall    0.620968
   Jordan Leavitt    0.610811
    Sean O'Malley    0.610329
Umar Nurmagomedov    0.596026
     Sean Soriano    0.595349
       Ciryl Gane    0.591365


Top ten fighters for CLINCH_%:
               FIGHTER  CLINCH_%
     Umar Nurmagomedov       1.0
             Joe Pyfer       1.0
           Mike Malott       1.0
        Yohan Lainesse       1.0
          Rafael Alves       1.0
      Michael Chandler       1.0
          Vanessa Melo       1.0
     Chance Rencountre       1.0
Alvaro Herrera Mendoza       1.0
            Dongi Yang       1.0


Top ten fighters for GROUND_%:
           FIGHTER  GROUND_%
   Marcin Prachnio       1.0
         Zac Pauga       1.0
    Yohan Lainesse       1.0
  Carlos Hernandez       1.0
        Trey Ogden       1.0
Modestas Bukauskas       1.0
     Hakeem Dawodu       1.0
           JP Buys       1.0
      Aleksa Camur       1.0
    Journey Newson       1.0


Top ten fighters for SIG_%:
          FIGHTER    SIG_%
   Danilo Marques 0.797101
 Alistair Overeem 0.743243
  Cyrille Diabate 0.727794
     Brock Lesnar 0.719595
    Tatsuro Taira 0.710204
Umar Nurmagomedov 0.699367
 Bartosz Fabinski 0.680723
   Darrick Minner 0.674556
     Tom Aspinall 0.666667
       Ryan Jimmo 0.661342


Top ten fighters for STR_%:
          FIGHTER    STR_%
     Brock Lesnar 0.824593
   Eugene Jackson 0.820225
      Mike Guymon 0.813830
   Aaron Phillips 0.792910
 Bartosz Fabinski 0.792208
     Pat Sabatini 0.789168
   Darrick Minner 0.779605
     Tony DeSouza 0.778947
 Alistair Overeem 0.778512
Charlie Brenneman 0.773613


Top ten fighters for TD_DEF_%:
          FIGHTER  TD_DEF_%
  Jailton Almeida       1.0
     Philipe Lins       1.0
Umar Nurmagomedov       1.0
  Yazmin Jauregui       1.0
Gregory Rodrigues       1.0
   Rodolfo Vieira       1.0
        Zac Pauga       1.0
     Martin Buday       1.0
Shavkat Rakhmonov       1.0
       Trey Ogden       1.0


Top ten fighters for HEAD_DEF_%:
          FIGHTER  HEAD_DEF_%
       Jon Madsen    0.902913
Umar Nurmagomedov    0.878049
    Miguel Torres    0.861607
  Adriano Martins    0.836449
   Claude Patrick    0.825243
        Nate Mohr    0.816901
   Montel Jackson    0.814978
    Desmond Green    0.813780
       Fares Ziam    0.812500
     Tom Aspinall    0.812500


Top ten fighters for BODY_DEF_%:
         FIGHTER  BODY_DEF_%
      Jason Witt    1.000000
  Ivan Salaverry    0.714286
    Hunter Azure    0.641509
     Frank Trigg    0.625000
Viscardi Andrade    0.611111
  Luana Pinheiro    0.608696
   Kajan Johnson    0.607143
     Mike Guymon    0.600000
     Phil Harris    0.589744
       Nate Mohr    0.578947


Top ten fighters for LEG_DEF_%:
        FIGHTER  LEG_DEF_%
     Jason Witt   0.600000
    Vaughan Lee   0.600000
    Mike Malott   0.571429
   Kyle Bradley   0.571429
Muhammad Mokaev   0.562500
Anthony Birchak   0.545455
 Jordan Leavitt   0.540541
 Tatiana Suarez   0.533333
 Naoyuki Kotani   0.529412
 Darrick Minner   0.500000


Top ten fighters for DISTANCE_DEF_%:
         FIGHTER  DISTANCE_DEF_%
      Jon Madsen        0.856707
     Marcio Cruz        0.830189
  Claude Patrick        0.827273
  Ivan Salaverry        0.824675
     Rory Singer        0.812081
   Miguel Torres        0.809524
 Jailton Almeida        0.789474
Luigi Fioravanti        0.788955
      Luke Cummo        0.788732
 Kevin Randleman        0.784615


Top ten fighters for CLINCH_DEF_%:
         FIGHTER  CLINCH_DEF_%
      Trey Ogden      1.000000
    Adrian Yanez      0.800000
     Ryan Jensen      0.750000
   Tony Ferguson      0.687500
  Jason Gonzalez      0.687500
      Ben Nguyen      0.684211
     Mike Malott      0.666667
   Kazula Vargas      0.666667
      Greg Hardy      0.666667
Curtis Millender      0.666667


Top ten fighters for GROUND_DEF_%:
          FIGHTER  GROUND_DEF_%
 Daniel Zellhuber      1.000000
  Michael Morales      1.000000
       Trey Ogden      1.000000
     Tom Aspinall      1.000000
 Rashid Magomedov      1.000000
   Sergey Morozov      0.750000
    Fredy Serrano      0.714286
 Darren Uyenoyama      0.714286
Kennedy Nzechukwu      0.666667
Victoria Leonardo      0.666667


Top ten fighters for SIG_DEF_%:
          FIGHTER  SIG_DEF_%
       Jon Madsen   0.813889
Umar Nurmagomedov   0.761468
    Miguel Torres   0.755245
        Nate Mohr   0.749245
Georges St-Pierre   0.730727
       Phil Davis   0.723457
   Ivan Salaverry   0.717703
       Ryan Bader   0.717542
  Danielle Taylor   0.716895
      Miles Johns   0.715736


Top ten fighters for STR_DEF_%:
        FIGHTER  STR_DEF_%
     Jon Madsen   0.758883
      Nate Mohr   0.710227
 Aiemann Zahabi   0.702062
Danielle Taylor   0.700000
    Miles Johns   0.681710
Adriano Martins   0.680233
  John Makdessi   0.671558
     Phil Davis   0.669291
Vinny Magalhaes   0.661448
       Joe Soto   0.657095


# Champions
c_champions = career[career['EITHER_CHAMP'] == 'Yes']

c_champions


c_champions.describe()


# List of columns to calculate means
columns_of_interest = [
    "KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
    "HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
    "GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
    "HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
    "CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]

# Calculate the mean for each of the specified columns
means1 = c_champions[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means1.plot(kind='bar', color='gold')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

STR_PERCENTILE             87.157449
HEAD_PERCENTILE            86.612409
SIG_PERCENTILE             86.254187
GROUND_PERCENTILE          85.805948
CLINCH_PERCENTILE          84.387652
BODY_PERCENTILE            83.800070
HEAD_DEF_PERCENTILE        83.239616
SIG_DEF_PERCENTILE         83.157819
STR_DEF_PERCENTILE         83.047249
DISTANCE_PERCENTILE        82.934007
DISTANCE_DEF_PERCENTILE    82.739585
CTRL_PERCENTILE            82.224962
KD_PERCENTILE              81.512218
LEG_PERCENTILE             81.137349
TD_DEF_PERCENTILE          79.541073
TD_PERCENTILE              79.423516
LEG_DEF_PERCENTILE         79.246152
BODY_DEF_PERCENTILE        78.732145
CLINCH_DEF_PERCENTILE      78.587048
SUB.ATT_PERCENTILE         75.471566
GROUND_DEF_PERCENTILE      70.326572
REV_PERCENTILE             62.787060
STR_%_PERCENTILE           60.918044
dtype: float64


# List of columns to calculate means
columns_of_interest = [
    "TD_%",	'HEAD_%', 'BODY_%',	'LEG_%', 'DISTANCE_%',	'CLINCH_%',	'GROUND_%',	'SIG_%', 'STR_%', 'TD_DEF_%',
    'HEAD_DEF_%',	'BODY_DEF_%', 'LEG_DEF_%',	'DISTANCE_DEF_%',	'CLINCH_DEF_%',	'GROUND_DEF_%',	'SIG_DEF_%', 'STR_DEF_%'
]

# Calculate the mean for each of the specified columns
meansA = c_champions[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_meansA = meansA.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansA.plot(kind='bar', color='rebeccapurple')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansA)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

LEG_%             0.842307
BODY_%            0.752657
CLINCH_%          0.714435
GROUND_%          0.692284
TD_DEF_%          0.662773
HEAD_DEF_%        0.656814
DISTANCE_DEF_%    0.625127
STR_%             0.572208
SIG_DEF_%         0.568168
SIG_%             0.485006
STR_DEF_%         0.481506
TD_%              0.470467
DISTANCE_%        0.413881
HEAD_%            0.404081
GROUND_DEF_%      0.309731
CLINCH_DEF_%      0.309558
BODY_DEF_%        0.299603
LEG_DEF_%         0.201431
dtype: float64


# Challengers
c_challengers = career[career['EVER_CHALLENGER'] == 'Yes']

c_challengers


c_challengers.describe()


# List of columns to calculate means
columns_of_interest = [
    "KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
    "HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
    "GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
    "HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
    "CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]

# Calculate the mean for each of the specified columns
means2 = c_challengers[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means2.plot(kind='bar', color='silver')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means2)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

STR_PERCENTILE             82.380380
GROUND_PERCENTILE          81.194476
HEAD_PERCENTILE            81.138135
SIG_PERCENTILE             80.868243
HEAD_DEF_PERCENTILE        79.846873
STR_DEF_PERCENTILE         79.824805
SIG_DEF_PERCENTILE         79.733994
CLINCH_PERCENTILE          79.283947
DISTANCE_DEF_PERCENTILE    78.909477
BODY_PERCENTILE            78.360612
CTRL_PERCENTILE            78.282105
DISTANCE_PERCENTILE        77.663619
CLINCH_DEF_PERCENTILE      76.950613
LEG_PERCENTILE             76.493828
LEG_DEF_PERCENTILE         76.238973
TD_DEF_PERCENTILE          76.084498
KD_PERCENTILE              75.908053
TD_PERCENTILE              75.432423
BODY_DEF_PERCENTILE        75.243381
GROUND_DEF_PERCENTILE      71.705494
SUB.ATT_PERCENTILE         71.692702
REV_PERCENTILE             62.963010
STR_%_PERCENTILE           55.675548
dtype: float64


# List of columns to calculate means
columns_of_interest = [
    "TD_%",	'HEAD_%', 'BODY_%',	'LEG_%', 'DISTANCE_%',	'CLINCH_%',	'GROUND_%',	'SIG_%', 'STR_%', 'TD_DEF_%',
    'HEAD_DEF_%',	'BODY_DEF_%', 'LEG_DEF_%',	'DISTANCE_DEF_%',	'CLINCH_DEF_%',	'GROUND_DEF_%',	'SIG_DEF_%', 'STR_DEF_%'
]

# Calculate the mean for each of the specified columns
meansB = c_challengers[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_meansB = meansB.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansB.plot(kind='bar', color='turquoise')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansB)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

LEG_%             0.817924
BODY_%            0.727326
CLINCH_%          0.706195
GROUND_%          0.688538
HEAD_DEF_%        0.639834
TD_DEF_%          0.625632
DISTANCE_DEF_%    0.618785
STR_%             0.556207
SIG_DEF_%         0.554091
STR_DEF_%         0.462211
SIG_%             0.461697
TD_%              0.416920
DISTANCE_%        0.389120
HEAD_%            0.380254
CLINCH_DEF_%      0.309191
GROUND_DEF_%      0.296140
BODY_DEF_%        0.293381
LEG_DEF_%         0.194047
dtype: float64


# Non Champions
c_non_champions = career[career['EITHER_CHAMP'] != 'Yes']

c_non_champions


c_non_champions.describe()


# List of columns to calculate means
columns_of_interest = [
    "KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
    "HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
    "GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
    "HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
    "CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]

# Calculate the mean for each of the specified columns
means3 = c_non_champions[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means3.plot(kind='bar', color='orangered')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means3)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

STR_%_PERCENTILE           49.504118
REV_PERCENTILE             49.415623
GROUND_DEF_PERCENTILE      49.057735
SUB.ATT_PERCENTILE         48.813511
CLINCH_DEF_PERCENTILE      48.665624
BODY_DEF_PERCENTILE        48.658736
LEG_DEF_PERCENTILE         48.634337
TD_PERCENTILE              48.625918
TD_DEF_PERCENTILE          48.620338
LEG_PERCENTILE             48.544565
KD_PERCENTILE              48.526771
CTRL_PERCENTILE            48.492938
DISTANCE_DEF_PERCENTILE    48.468510
DISTANCE_PERCENTILE        48.459281
STR_DEF_PERCENTILE         48.453905
SIG_DEF_PERCENTILE         48.448657
HEAD_DEF_PERCENTILE        48.444774
BODY_PERCENTILE            48.418170
CLINCH_PERCENTILE          48.390279
GROUND_PERCENTILE          48.322955
SIG_PERCENTILE             48.301677
HEAD_PERCENTILE            48.284673
STR_PERCENTILE             48.258801
dtype: float64


# List of columns to calculate means
columns_of_interest = [
    "TD_%",	'HEAD_%', 'BODY_%',	'LEG_%', 'DISTANCE_%',	'CLINCH_%',	'GROUND_%',	'SIG_%', 'STR_%', 'TD_DEF_%',
    'HEAD_DEF_%',	'BODY_DEF_%', 'LEG_DEF_%',	'DISTANCE_DEF_%',	'CLINCH_DEF_%',	'GROUND_DEF_%',	'SIG_DEF_%', 'STR_DEF_%'
]

# Calculate the mean for each of the specified columns
meansC = c_non_champions[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_meansC = meansC.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansC.plot(kind='bar', color='palevioletred')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansC)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

LEG_%             0.794655
BODY_%            0.684252
GROUND_%          0.678273
CLINCH_%          0.666827
HEAD_DEF_%        0.605819
DISTANCE_DEF_%    0.605248
TD_DEF_%          0.556787
STR_%             0.529423
SIG_DEF_%         0.523505
SIG_%             0.434385
STR_DEF_%         0.433996
DISTANCE_%        0.370481
TD_%              0.363704
HEAD_%            0.342294
CLINCH_DEF_%      0.303750
GROUND_DEF_%      0.296509
BODY_DEF_%        0.289229
LEG_DEF_%         0.188687
dtype: float64


# Non Challengers
c_non_challengers = career[career['EVER_CHALLENGER'] != 'Yes']

c_non_challengers


c_non_challengers.describe()


# List of columns to calculate means
columns_of_interest = [
    "KD_PERCENTILE", "TD_PERCENTILE", "SUB.ATT_PERCENTILE", "REV_PERCENTILE", "CTRL_PERCENTILE",
    "HEAD_PERCENTILE", "BODY_PERCENTILE", "LEG_PERCENTILE", "DISTANCE_PERCENTILE", "CLINCH_PERCENTILE",
    "GROUND_PERCENTILE", "SIG_PERCENTILE", "STR_PERCENTILE", "STR_%_PERCENTILE", "TD_DEF_PERCENTILE",
    "HEAD_DEF_PERCENTILE", "BODY_DEF_PERCENTILE", "LEG_DEF_PERCENTILE", "DISTANCE_DEF_PERCENTILE",
    "CLINCH_DEF_PERCENTILE", "GROUND_DEF_PERCENTILE", "SIG_DEF_PERCENTILE", "STR_DEF_PERCENTILE"
]

# Calculate the mean for each of the specified columns
means4 = c_non_challengers[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means4.plot(kind='bar', color='khaki')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means4)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

STR_%_PERCENTILE           49.425157
REV_PERCENTILE             48.657049
SUB.ATT_PERCENTILE         47.736600
GROUND_DEF_PERCENTILE      47.735251
BODY_DEF_PERCENTILE        47.362220
TD_PERCENTILE              47.342288
KD_PERCENTILE              47.292138
TD_DEF_PERCENTILE          47.273533
LEG_DEF_PERCENTILE         47.257246
LEG_PERCENTILE             47.230374
CLINCH_DEF_PERCENTILE      47.182211
DISTANCE_PERCENTILE        47.107032
CTRL_PERCENTILE            47.041820
BODY_PERCENTILE            47.033542
DISTANCE_DEF_PERCENTILE    46.975671
CLINCH_PERCENTILE          46.936187
SIG_DEF_PERCENTILE         46.888734
STR_DEF_PERCENTILE         46.879159
HEAD_DEF_PERCENTILE        46.876833
SIG_PERCENTILE             46.769140
HEAD_PERCENTILE            46.740683
GROUND_PERCENTILE          46.734743
STR_PERCENTILE             46.609702
dtype: float64


# List of columns to calculate means
columns_of_interest = [
    "TD_%",	'HEAD_%', 'BODY_%',	'LEG_%', 'DISTANCE_%',	'CLINCH_%',	'GROUND_%',	'SIG_%', 'STR_%', 'TD_DEF_%',
    'HEAD_DEF_%',	'BODY_DEF_%', 'LEG_DEF_%',	'DISTANCE_DEF_%',	'CLINCH_DEF_%',	'GROUND_DEF_%',	'SIG_DEF_%', 'STR_DEF_%'
]

# Calculate the mean for each of the specified columns
meansD = c_non_challengers[columns_of_interest].mean()

# Rank the means from highest to lowest and plot again
sorted_meansD = meansD.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansD.plot(kind='bar', color='indianred')
plt.title('Mean Percentiles across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansD)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

LEG_%             0.794597
BODY_%            0.683110
GROUND_%          0.677831
CLINCH_%          0.664954
DISTANCE_DEF_%    0.604814
HEAD_DEF_%        0.604784
TD_DEF_%          0.554721
STR_%             0.528742
SIG_DEF_%         0.522517
SIG_%             0.434040
STR_DEF_%         0.433401
DISTANCE_%        0.370691
TD_%              0.363409
HEAD_%            0.341382
CLINCH_DEF_%      0.303448
GROUND_DEF_%      0.297266
BODY_DEF_%        0.289315
LEG_DEF_%         0.188770
dtype: float64


fight_average.describe()


metrics1 = ['FA_KD', 'FA_TD', 'FA_SUB.ATT', 'FA_REV', 'FA_CTRL', 'FA_HEAD', 'FA_BODY', 'FA_LEG', 'FA_DISTANCE', 
    'FA_CLINCH', 'FA_GROUND', 'FA_SIG', 'FA_STR']


metrics2 = [
    'FA_TD_%','FA_HEAD_%','FA_BODY_%','FA_LEG_%','FA_DISTANCE_%','FA_CLINCH_%',	'FA_GROUND_%', 'FA_SIG_%',
    'FA_STR_%',	'FA_TD_DEF_%','FA_HEAD_DEF_%','FA_BODY_DEF_%','FA_LEG_DEF_%', 'FA_DISTANCE_DEF_%',	
    'FA_CLINCH_DEF_%',	'FA_GROUND_DEF_%',	'FA_SIG_DEF_%',	'FA_STR_DEF_%']


# Filter the dataframe using the correct column name "FIGHTER"
fa = fight_average[fight_average['FIGHTER'].isin(fighters_list)]

fa


# Original metrics list
original_metrics = [
    "KD", "TD", "SUB.ATT", "REV", "CTRL", "HEAD", "BODY", "LEG", "DISTANCE",
    "CLINCH", "GROUND", "SIG", "STR", "TD_DEF", "HEAD_DEF", "BODY_DEF", "LEG_DEF",
    "DISTANCE_DEF", "CLINCH_DEF", "GROUND_DEF", "SIG_DEF", "STR_DEF", "TD_%",
    "HEAD_%", "BODY_%", "LEG_%", "DISTANCE_%", "CLINCH_%", "GROUND_%", "SIG_%",
    "STR_%", "TD_DEF_%", "HEAD_DEF_%", "BODY_DEF_%", "LEG_DEF_%", "DISTANCE_DEF_%",
    "CLINCH_DEF_%", "GROUND_DEF_%", "SIG_DEF_%", "STR_DEF_%"
]

# Adding 'FA_' prefix to each metric in the list
fa_metrics = ["FA_" + metric for metric in original_metrics]
fa_metrics

['FA_KD',
 'FA_TD',
 'FA_SUB.ATT',
 'FA_REV',
 'FA_CTRL',
 'FA_HEAD',
 'FA_BODY',
 'FA_LEG',
 'FA_DISTANCE',
 'FA_CLINCH',
 'FA_GROUND',
 'FA_SIG',
 'FA_STR',
 'FA_TD_DEF',
 'FA_HEAD_DEF',
 'FA_BODY_DEF',
 'FA_LEG_DEF',
 'FA_DISTANCE_DEF',
 'FA_CLINCH_DEF',
 'FA_GROUND_DEF',
 'FA_SIG_DEF',
 'FA_STR_DEF',
 'FA_TD_%',
 'FA_HEAD_%',
 'FA_BODY_%',
 'FA_LEG_%',
 'FA_DISTANCE_%',
 'FA_CLINCH_%',
 'FA_GROUND_%',
 'FA_SIG_%',
 'FA_STR_%',
 'FA_TD_DEF_%',
 'FA_HEAD_DEF_%',
 'FA_BODY_DEF_%',
 'FA_LEG_DEF_%',
 'FA_DISTANCE_DEF_%',
 'FA_CLINCH_DEF_%',
 'FA_GROUND_DEF_%',
 'FA_SIG_DEF_%',
 'FA_STR_DEF_%']


# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}

# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}

# Finding the top ten fighters for each metric
for metric in fa_metrics:
    # Some metrics may have missing values, so we sort only non-NA values
    top_fighters_per_metric[metric] = fa.nlargest(10, metric)[['FIGHTER', metric]]

# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
    print(f"Top ten fighters for {metric}:")
    if isinstance(data, pd.DataFrame):
        print(data.to_string(index=False))
    else:
        print(data)
    print("\n")  # Blank line for separation

Top ten fighters for FA_KD:
             FIGHTER    FA_KD
    Sergei Pavlovich 0.875000
Jack Della Maddalena 0.761905
      Danaa Batgerel 0.750000
        Steve Garcia 0.666667
         Todd Duffee 0.666667
      Drew McFedries 0.666667
    Punahele Soriano 0.642857
Abdul Razak Alhassan 0.625000
       Chris Daukaus 0.625000
      Albert Tumenov 0.625000


Top ten fighters for FA_TD:
              FIGHTER    FA_TD
    Merab Dvalishvili 2.127778
      Jailton Almeida 2.028571
       Tatiana Suarez 1.880952
        Jacob Malkoun 1.833333
       Jeremy Kennedy 1.750000
Zabit Magomedsharipov 1.694444
      Muhammad Mokaev 1.666667
     Gregor Gillespie 1.645833
        Karo Parisyan 1.628205
       Serghei Spivac 1.590909


Top ten fighters for FA_SUB.ATT:
          FIGHTER  FA_SUB.ATT
      James Wilks    2.333333
        Paul Sass    2.133333
      John Albert    1.500000
  Shannon Gugerty    1.166667
    TJ Waldburger    1.125000
  Khamzat Chimaev    1.083333
   Ramiz Brahimaj    1.083333
  Dustin Hazelett    1.083333
Rousimar Palhares    1.027778
      Rob Kimmons    0.952381


Top ten fighters for FA_REV:
         FIGHTER   FA_REV
 Cameron Saaiman 0.750000
 Yosdenis Cedeno 0.583333
  Alvin Robinson 0.500000
Michinori Tanaka 0.466667
  Jerrod Sanders 0.458333
   Cody McKenzie 0.428571
     Dan Argueta 0.416667
       Luis Pena 0.416667
 Veronica Macedo 0.400000
   Heath Herring 0.400000


Top ten fighters for FA_CTRL:
           FIGHTER  FA_CTRL
   Jailton Almeida 3.565476
     Tatsuro Taira 2.877222
    Tatiana Suarez 2.849603
Rinat Fakhretdinov 2.716667
      Chael Sonnen 2.713611
       Joe Solecki 2.692460
  Gregor Gillespie 2.659375
      Grant Dawson 2.611111
     Jacob Malkoun 2.593519
     Matt Lindland 2.550926


Top ten fighters for FA_HEAD:
          FIGHTER   FA_HEAD
    Casey O'Neill 27.583333
Billy Quarantillo 22.962963
  Sean Strickland 22.714286
     Max Holloway 22.256790
    Lavar Johnson 22.250000
      David Onama 21.766667
 Daniel Rodriguez 21.650000
     Shane Burgos 20.909091
     Leslie Smith 20.880952
  Michael Morales 20.583333


Top ten fighters for FA_BODY:
          FIGHTER   FA_BODY
       Kai Kamaka 10.250000
      Tony Kelley 10.208333
      Paulo Costa  9.544444
       CJ Vergara  8.861111
 Loma Lookboonmee  8.750000
     John Lineker  8.666667
   Michel Pereira  8.333333
Cristiane Justino  8.304762
    Diana Belbita  8.142857
 Joselyne Edwards  8.095238


Top ten fighters for FA_LEG:
          FIGHTER    FA_LEG
  Armen Petrosyan 11.666667
  Chris Gutierrez 11.169444
Marcelo Guimaraes 10.583333
      Joanne Wood 10.215686
   Nathaniel Wood  9.851852
  Marcin Prachnio  8.870370
       Karol Rosa  8.777778
    Sarah Kaufman  8.750000
   Justin Gaethje  8.180556
     Emily Ducote  7.833333


Top ten fighters for FA_DISTANCE:
            FIGHTER  FA_DISTANCE
       Emily Ducote    33.333333
 Ignacio Bahamondes    30.200000
   Daniel Rodriguez    29.483333
Waldo Cortes-Acosta    28.333333
       Sean Woodson    27.809524
      Sean O'Malley    27.297222
       Max Holloway    27.126543
      Casey O'Neill    26.972222
       Shane Burgos    26.909091
          Ian Garry    25.611111


Top ten fighters for FA_CLINCH:
             FIGHTER  FA_CLINCH
       Lavar Johnson  14.166667
       Sarah Kaufman  12.375000
        Leslie Smith   8.571429
        Cody Donovan   8.375000
     Timothy Johnson   8.142857
       Maycee Barber   7.954545
  Isaac Vallie-Flagg   7.916667
Marc-Andre Barriault   7.777778
     Jessica Aguilar   7.600000
    Loma Lookboonmee   7.458333


Top ten fighters for FA_GROUND:
            FIGHTER  FA_GROUND
    Khamzat Chimaev  15.055556
    Jailton Almeida  13.714286
     Cain Velasquez  11.711111
Khabib Nurmagomedov  10.901282
   Gregor Gillespie  10.062500
         Matt Grice   9.761905
     Tatiana Suarez   9.571429
        Zhang Weili   9.133333
       Brock Lesnar   8.979167
   Erin Blanchfield   8.933333


Top ten fighters for FA_SIG:
           FIGHTER    FA_SIG
     Casey O'Neill 35.277778
      Emily Ducote 34.666667
     Sarah Kaufman 34.458333
      Shane Burgos 32.545455
 Billy Quarantillo 32.388889
Ignacio Bahamondes 32.333333
      Max Holloway 32.283951
        Karol Rosa 31.703704
  Daniel Rodriguez 31.183333
       Joanne Wood 30.607843


Top ten fighters for FA_STR:
             FIGHTER    FA_STR
       Casey O'Neill 54.500000
   Merab Dvalishvili 52.450000
       Sarah Kaufman 51.583333
   Billy Quarantillo 50.407407
    Erin Blanchfield 50.300000
          Karol Rosa 50.037037
      Cain Velasquez 48.120000
Jasmine Jasudavicius 45.777778
           Nick Diaz 45.604444
       Pascal Krauss 44.833333


Top ten fighters for FA_TD_DEF:
           FIGHTER  FA_TD_DEF
   Charles Johnson   2.904762
    Michael Kuiper   2.833333
       Diego Nunes   2.722222
    Taylor Lapilus   2.694444
      Austin Lingo   2.300000
    Gasan Umalatov   2.250000
Joanna Jedrzejczyk   2.215556
    Shane Campbell   2.200000
      Sean Woodson   2.190476
       Ian Freeman   2.166667


Top ten fighters for FA_HEAD_DEF:
         FIGHTER  FA_HEAD_DEF
        Nam Phan    54.416667
    Emily Ducote    48.416667
    Vanessa Melo    41.333333
    Irene Aldana    38.087179
  Jodie Esquibel    37.666667
 Danielle Taylor    37.266667
Nasrat Haqparast    37.125000
Alptekin Ozkilic    36.083333
      Mike Davis    35.875000
    Shane Burgos    35.060606


Top ten fighters for FA_BODY_DEF:
        FIGHTER  FA_BODY_DEF
     Trey Ogden     5.750000
 Luana Pinheiro     4.833333
Sean Strickland     4.649206
    Ji Yeon Kim     4.550000
   Austin Lingo     4.533333
       Nam Phan     4.458333
     Manel Kape     4.222222
   Casey Kenney     4.083333
     Andrea Lee     3.972222
   Sean Woodson     3.904762


Top ten fighters for FA_LEG_DEF:
         FIGHTER  FA_LEG_DEF
  Luana Pinheiro    3.583333
 Poliana Botelho    2.904762
     Seo Hee Ham    2.083333
 Danielle Taylor    2.066667
    Alatengheili    2.047619
  Cody Garbrandt    2.019048
     Andre Ewell    2.018519
Daniel Zellhuber    2.000000
 Viviane Pereira    2.000000
     Hatsu Hioki    2.000000


Top ten fighters for FA_DISTANCE_DEF:
         FIGHTER  FA_DISTANCE_DEF
        Nam Phan        57.750000
    Emily Ducote        51.500000
    Vanessa Melo        42.666667
 Danielle Taylor        41.200000
    Irene Aldana        40.835897
Nasrat Haqparast        39.652778
  Jodie Esquibel        38.916667
   John Makdessi        38.358333
     Seo Hee Ham        37.666667
    Shane Burgos        37.484848


Top ten fighters for FA_CLINCH_DEF:
          FIGHTER  FA_CLINCH_DEF
  Wesley Correira       6.000000
 Stanislav Nedkov       5.208333
   Julian Marquez       4.444444
     Andre Winner       4.333333
Jonathan Brookins       4.233333
 Dominique Steele       4.066667
    Alex Chambers       4.066667
  Jonathan Wilson       3.916667
      Josh Grispi       3.666667
   Paul Buentello       3.638889


Top ten fighters for FA_GROUND_DEF:
             FIGHTER  FA_GROUND_DEF
        Edgar Garcia      10.000000
       Elvis Sinosic       9.261905
          Jason Dent       6.708333
        Terrion Ware       6.250000
Germaine de Randamie       6.103704
     Seth Petruzelli       6.000000
         Jenel Lausa       5.458333
      Jeremy Kennedy       5.375000
      Danilo Marques       5.166667
      Naoyuki Kotani       5.133333


Top ten fighters for FA_SIG_DEF:
         FIGHTER  FA_SIG_DEF
        Nam Phan   59.666667
    Emily Ducote   52.083333
    Vanessa Melo   44.416667
    Irene Aldana   42.543590
 Danielle Taylor   41.866667
  Jodie Esquibel   40.833333
     Seo Hee Ham   40.500000
Alptekin Ozkilic   40.333333
Nasrat Haqparast   39.986111
   John Makdessi   39.466667


Top ten fighters for FA_STR_DEF:
         FIGHTER  FA_STR_DEF
        Nam Phan   61.958333
    Emily Ducote   52.500000
    Vanessa Melo   45.416667
    Irene Aldana   43.543590
 Danielle Taylor   42.000000
     Seo Hee Ham   41.583333
  Jodie Esquibel   40.916667
Alptekin Ozkilic   40.666667
     Syuri Kondo   40.583333
Nasrat Haqparast   40.486111


Top ten fighters for FA_TD_%:
         FIGHTER  FA_TD_%
   Johnny Walker      1.0
      Sumudaerji      1.0
    Jared Gooden      1.0
      Uros Medic      1.0
    Alex Pereira      1.0
    Tom Aspinall      1.0
    Andre Fialho      1.0
    Omar Morales      1.0
Cheyanne Vlismas      1.0
  Mariya Agapova      1.0


Top ten fighters for FA_HEAD_%:
          FIGHTER  FA_HEAD_%
   Danilo Marques   0.816794
    Tatsuro Taira   0.660944
Umar Nurmagomedov   0.660028
  Cyrille Diabate   0.653291
 Alistair Overeem   0.649761
  Khamzat Chimaev   0.640294
  Jailton Almeida   0.638627
    Gunnar Nelson   0.631987
     Brock Lesnar   0.614583
   Manny Bermudez   0.608696


Top ten fighters for FA_BODY_%:
          FIGHTER  FA_BODY_%
   Jeremy Kimball        1.0
   Jerrod Sanders        1.0
        Paul Sass        1.0
        Ken Stone        1.0
     Shane Carwin        1.0
Chris Tuchscherer        1.0
     Jake O'Brien        1.0
   Alvin Robinson        1.0
      Rory Singer        1.0
      Marcio Cruz        1.0


Top ten fighters for FA_LEG_%:
         FIGHTER  FA_LEG_%
   Nikolas Motta       1.0
    Martin Buday       1.0
    Steve Garcia       1.0
     Joe Solecki       1.0
      Uros Medic       1.0
Sergei Pavlovich       1.0
    Tom Aspinall       1.0
     Nate Maness       1.0
   Jacob Malkoun       1.0
     Zarah Fairn       1.0


Top ten fighters for FA_DISTANCE_%:
             FIGHTER  FA_DISTANCE_%
      Danilo Marques       0.682927
     Cyrille Diabate       0.666168
    Alistair Overeem       0.653726
Francisco Figueiredo       0.651515
       Tatsuro Taira       0.632479
   Umar Nurmagomedov       0.629032
        Tom Aspinall       0.625000
          Ciryl Gane       0.611429
       Sean O'Malley       0.608219
      Darrick Minner       0.599138


Top ten fighters for FA_CLINCH_%:
               FIGHTER  FA_CLINCH_%
     Umar Nurmagomedov          1.0
             Joe Pyfer          1.0
           Mike Malott          1.0
        Yohan Lainesse          1.0
          Rafael Alves          1.0
      Michael Chandler          1.0
          Vanessa Melo          1.0
     Chance Rencountre          1.0
Alvaro Herrera Mendoza          1.0
            Dongi Yang          1.0


Top ten fighters for FA_GROUND_%:
           FIGHTER  FA_GROUND_%
   Marcin Prachnio          1.0
         Zac Pauga          1.0
    Yohan Lainesse          1.0
  Carlos Hernandez          1.0
        Trey Ogden          1.0
Modestas Bukauskas          1.0
     Hakeem Dawodu          1.0
           JP Buys          1.0
      Aleksa Camur          1.0
    Journey Newson          1.0


Top ten fighters for FA_SIG_%:
             FIGHTER  FA_SIG_%
      Danilo Marques  0.832335
    Alistair Overeem  0.738928
     Cyrille Diabate  0.717838
       Tatsuro Taira  0.710297
      Darrick Minner  0.707265
   Umar Nurmagomedov  0.696228
        Tom Aspinall  0.675439
        Brock Lesnar  0.673729
       Jordan Wright  0.667735
Francisco Figueiredo  0.664557


Top ten fighters for FA_STR_%:
         FIGHTER  FA_STR_%
    Tony DeSouza  0.794118
  Darrick Minner  0.792929
    Brock Lesnar  0.791922
  Danilo Marques  0.790503
  Aaron Phillips  0.787684
   Jordan Wright  0.782555
  Eugene Jackson  0.778626
Alistair Overeem  0.770993
     Nate Maness  0.770844
    Pat Sabatini  0.765889


Top ten fighters for FA_TD_DEF_%:
          FIGHTER  FA_TD_DEF_%
  Jailton Almeida          1.0
     Philipe Lins          1.0
Umar Nurmagomedov          1.0
  Yazmin Jauregui          1.0
Gregory Rodrigues          1.0
   Rodolfo Vieira          1.0
        Zac Pauga          1.0
     Martin Buday          1.0
Shavkat Rakhmonov          1.0
       Trey Ogden          1.0


Top ten fighters for FA_HEAD_DEF_%:
           FIGHTER  FA_HEAD_DEF_%
        Jon Madsen       0.889740
   Adriano Martins       0.851585
 Umar Nurmagomedov       0.848684
     Miguel Torres       0.829341
    Claude Patrick       0.821256
      Manon Fiorot       0.819853
Demetrious Johnson       0.818847
      Tom Aspinall       0.815217
     Desmond Green       0.814208
    Montel Jackson       0.812734


Top ten fighters for FA_BODY_DEF_%:
         FIGHTER  FA_BODY_DEF_%
      Jason Witt       1.000000
    Hunter Azure       0.650407
  Ivan Salaverry       0.636364
     Frank Trigg       0.612903
Viscardi Andrade       0.611111
Michael Chandler       0.578125
   Kajan Johnson       0.566667
   Desmond Green       0.564286
      Ryan Janes       0.557692
      Myles Jury       0.548936


Top ten fighters for FA_LEG_DEF_%:
        FIGHTER  FA_LEG_DEF_%
   Charles Byrd      0.666667
    Josh Samman      0.666667
    Mike Malott      0.650000
    Vaughan Lee      0.647059
Muhammad Mokaev      0.611111
     Jason Witt      0.600000
Anthony Birchak      0.595238
 Jordan Leavitt      0.565217
      Che Mills      0.560976
   Kyle Bradley      0.545455


Top ten fighters for FA_DISTANCE_DEF_%:
       FIGHTER  FA_DISTANCE_DEF_%
    Jon Madsen           0.847605
   Marcio Cruz           0.828125
Ivan Salaverry           0.826316
Claude Patrick           0.819820
   Rory Singer           0.802667
 Miguel Torres           0.791209
 Kenny Florian           0.786079
     Che Mills           0.782609
    Luke Cummo           0.781955
    Terry Etim           0.774265


Top ten fighters for FA_CLINCH_DEF_%:
         FIGHTER  FA_CLINCH_DEF_%
      Trey Ogden         1.000000
   Kazula Vargas         0.857143
    Adrian Yanez         0.769231
   Zhang Tiequan         0.750000
   Tony Ferguson         0.710956
Curtis Millender         0.666667
Robert Whiteford         0.666667
     Ryan Jensen         0.666667
  Jason Gonzalez         0.653846
     Al Iaquinta         0.652174


Top ten fighters for FA_GROUND_DEF_%:
          FIGHTER  FA_GROUND_DEF_%
 Daniel Zellhuber         1.000000
  Michael Morales         1.000000
       Trey Ogden         1.000000
     Tom Aspinall         1.000000
 Rashid Magomedov         1.000000
Kennedy Nzechukwu         0.771930
   Johnny Bedford         0.750000
    Fredy Serrano         0.714286
 Darren Uyenoyama         0.710280
    Calvin Kattar         0.705882


Top ten fighters for FA_SIG_DEF_%:
            FIGHTER  FA_SIG_DEF_%
         Jon Madsen      0.804749
      Miguel Torres      0.747525
         Phil Davis      0.741087
          Nate Mohr      0.728291
  Umar Nurmagomedov      0.726190
    Danielle Taylor      0.716895
        Miles Johns      0.716372
Mara Romero Borella      0.707038
       Manon Fiorot      0.705787
  Georges St-Pierre      0.705286


Top ten fighters for FA_STR_DEF_%:
             FIGHTER  FA_STR_DEF_%
          Jon Madsen      0.734503
     Danielle Taylor      0.700000
          Phil Davis      0.695577
      Aiemann Zahabi      0.690476
         Miles Johns      0.683609
     Adriano Martins      0.679681
Jack Della Maddalena      0.668246
       Miguel Torres      0.664596
        Manon Fiorot      0.661639
           Nate Mohr      0.660000


fa_champions = fa[fa['EITHER_CHAMP'] == 'Yes']

fa_champions


# Calculate the mean for each of the specified columns
means1 = fa_champions[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means1.plot(kind='bar', color='dodgerblue')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_STR         25.885904
RA_SIG         17.712756
RA_DISTANCE    12.677297
RA_HEAD        11.715603
RA_BODY         3.407324
RA_GROUND       2.689004
RA_LEG          2.589829
RA_CLINCH       2.346455
RA_CTRL         1.002583
RA_TD           0.468739
RA_SUB.ATT      0.153378
RA_KD           0.092750
RA_REV          0.045344
dtype: float64


# Calculate the mean for each of the specified columns
meansA = fa_champions[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansA = meansA.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansA.plot(kind='bar', color='fuchsia')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansA)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_LEG_%             0.850586
RA_BODY_%            0.747112
RA_CLINCH_%          0.720817
RA_GROUND_%          0.697236
RA_TD_DEF_%          0.653601
RA_HEAD_DEF_%        0.643140
RA_DISTANCE_DEF_%    0.615557
RA_STR_%             0.571944
RA_SIG_DEF_%         0.561156
RA_SIG_%             0.484442
RA_STR_DEF_%         0.475463
RA_TD_%              0.472020
RA_DISTANCE_%        0.415333
RA_HEAD_%            0.406465
RA_GROUND_DEF_%      0.311422
RA_CLINCH_DEF_%      0.306687
RA_BODY_DEF_%        0.296030
RA_LEG_DEF_%         0.194439
dtype: float64


fa_challengers = fa[fa['EVER_CHALLENGER'] == 'Yes']

fa_challengers


# Calculate the mean for each of the specified columns
means2 = fa_challengers[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means2.plot(kind='bar', color='darkcyan')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means2)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_STR         23.677031
RA_SIG         15.668471
RA_DISTANCE    11.296105
RA_HEAD        10.307098
RA_BODY         3.061733
RA_GROUND       2.332257
RA_LEG          2.299641
RA_CLINCH       2.040109
RA_CTRL         0.935548
RA_TD           0.407687
RA_SUB.ATT      0.147046
RA_KD           0.088746
RA_REV          0.046639
dtype: float64


# Calculate the mean for each of the specified columns
meansB = fa_challengers[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansB = meansB.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansB.plot(kind='bar', color='peru')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansB)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_LEG_%             0.820616
RA_BODY_%            0.720454
RA_CLINCH_%          0.702440
RA_GROUND_%          0.688784
RA_HEAD_DEF_%        0.630248
RA_TD_DEF_%          0.613314
RA_DISTANCE_DEF_%    0.608496
RA_STR_%             0.547432
RA_SIG_DEF_%         0.547316
RA_STR_DEF_%         0.456533
RA_SIG_%             0.453429
RA_TD_%              0.402681
RA_DISTANCE_%        0.386544
RA_HEAD_%            0.375957
RA_CLINCH_DEF_%      0.309879
RA_GROUND_DEF_%      0.295873
RA_BODY_DEF_%        0.287543
RA_LEG_DEF_%         0.185764
dtype: float64


fa_non_champions = fa[fa['EITHER_CHAMP'] != 'Yes']

fa_non_champions


# Calculate the mean for each of the specified columns
means3 = fa_non_champions[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means3.plot(kind='bar', color='chartreuse')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means3)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_STR         22.930341
RA_SIG         15.091705
RA_DISTANCE    11.018353
RA_HEAD         9.655423
RA_BODY         3.090213
RA_LEG          2.346069
RA_CLINCH       2.088535
RA_GROUND       1.984818
RA_CTRL         0.943873
RA_TD           0.435841
RA_SUB.ATT      0.160861
RA_KD           0.079756
RA_REV          0.064868
dtype: float64


# Calculate the mean for each of the specified columns
meansC = fa_non_champions[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansC = meansC.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansC.plot(kind='bar', color='skyblue')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansC)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_LEG_%             0.808193
RA_BODY_%            0.696145
RA_GROUND_%          0.685055
RA_CLINCH_%          0.683619
RA_HEAD_DEF_%        0.625707
RA_DISTANCE_DEF_%    0.607792
RA_TD_DEF_%          0.595034
RA_SIG_DEF_%         0.543268
RA_STR_%             0.533661
RA_STR_DEF_%         0.454400
RA_SIG_%             0.443776
RA_DISTANCE_%        0.382282
RA_TD_%              0.374455
RA_HEAD_%            0.361605
RA_CLINCH_DEF_%      0.309204
RA_BODY_DEF_%        0.302528
RA_GROUND_DEF_%      0.291589
RA_LEG_DEF_%         0.186460
dtype: float64


fa_non_challengers = fa[fa['EVER_CHALLENGER'] != 'Yes']

fa_non_challengers


# Calculate the mean for each of the specified columns
means4 = fa_non_challengers[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means4.plot(kind='bar', color='slategray')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means4)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_STR         23.061549
RA_SIG         15.224483
RA_DISTANCE    11.119175
RA_HEAD         9.721662
RA_BODY         3.125167
RA_LEG          2.377654
RA_CLINCH       2.121820
RA_GROUND       1.983488
RA_CTRL         0.950932
RA_TD           0.444311
RA_SUB.ATT      0.162821
RA_KD           0.079236
RA_REV          0.066557
dtype: float64


# Calculate the mean for each of the specified columns
meansD = fa_non_challengers[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansD = meansD.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansD.plot(kind='bar', color='darksalmon')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_meansD)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

RA_LEG_%             0.809756
RA_BODY_%            0.696210
RA_GROUND_%          0.685475
RA_CLINCH_%          0.683458
RA_HEAD_DEF_%        0.626455
RA_DISTANCE_DEF_%    0.608378
RA_TD_DEF_%          0.596982
RA_SIG_DEF_%         0.544152
RA_STR_%             0.534572
RA_STR_DEF_%         0.455948
RA_SIG_%             0.445700
RA_DISTANCE_%        0.384535
RA_TD_%              0.378139
RA_HEAD_%            0.363016
RA_CLINCH_DEF_%      0.308838
RA_BODY_DEF_%        0.304807
RA_GROUND_DEF_%      0.292621
RA_LEG_DEF_%         0.187336
dtype: float64


round_average.describe()


metrics1 = ['RA_KD', 'RA_TD', 'RA_SUB.ATT', 'RA_REV', 'RA_CTRL', 'RA_HEAD', 'RA_BODY', 'RA_LEG', 'RA_DISTANCE', 
    'RA_CLINCH', 'RA_GROUND', 'RA_SIG', 'RA_STR']


metrics2 = [
    'RA_TD_%','RA_HEAD_%','RA_BODY_%','RA_LEG_%','RA_DISTANCE_%','RA_CLINCH_%',	'RA_GROUND_%', 'RA_SIG_%',
    'RA_STR_%',	'RA_TD_DEF_%','RA_HEAD_DEF_%','RA_BODY_DEF_%','RA_LEG_DEF_%', 'RA_DISTANCE_DEF_%',	
    'RA_CLINCH_DEF_%',	'RA_GROUND_DEF_%',	'RA_SIG_DEF_%',	'RA_STR_DEF_%']


# Filter the dataframe using the correct column name "FIGHTER"
ra = round_average[round_average['FIGHTER'].isin(fighters_list)]

ra


# Original metrics list
original_metrics = [
    "KD", "TD", "SUB.ATT", "REV", "CTRL", "HEAD", "BODY", "LEG", "DISTANCE",
    "CLINCH", "GROUND", "SIG", "STR", "TD_DEF", "HEAD_DEF", "BODY_DEF", "LEG_DEF",
    "DISTANCE_DEF", "CLINCH_DEF", "GROUND_DEF", "SIG_DEF", "STR_DEF", "TD_%",
    "HEAD_%", "BODY_%", "LEG_%", "DISTANCE_%", "CLINCH_%", "GROUND_%", "SIG_%",
    "STR_%", "TD_DEF_%", "HEAD_DEF_%", "BODY_DEF_%", "LEG_DEF_%", "DISTANCE_DEF_%",
    "CLINCH_DEF_%", "GROUND_DEF_%", "SIG_DEF_%", "STR_DEF_%"
]

# Adding 'RA_' prefix to each metric in the list
ra_metrics = ["RA_" + metric for metric in original_metrics]
ra_metrics


# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}

# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}

# Finding the top ten fighters for each metric
for metric in ra_metrics:
    # Some metrics may have missing values, so we sort only non-NA values
    top_fighters_per_metric[metric] = ra.nlargest(10, metric)[['FIGHTER', metric]]

# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
    print(f"Top ten fighters for {metric}:")
    if isinstance(data, pd.DataFrame):
        print(data.to_string(index=False))
    else:
        print(data)
    print("\n")  # Blank line for separation

Top ten fighters for RA_KD:
             FIGHTER    RA_KD
    Sergei Pavlovich 0.875000
Santiago Ponzinibbio 0.647839
          Uros Medic 0.622222
       Chris Daukaus 0.562500
      Michael Kuiper 0.555556
         James Irvin 0.550000
       Ihor Potieria 0.511111
 Joachim Christensen 0.500000
      Montel Jackson 0.481481
 Khalil Rountree Jr. 0.458333


Top ten fighters for RA_TD:
          FIGHTER    RA_TD
Merab Dvalishvili 2.446970
       Jason Witt 2.222222
    Jacob Malkoun 2.194444
     Jake O'Brien 2.142857
 Bartosz Fabinski 2.111111
   Jeremy Kennedy 2.027778
  Muhammad Mokaev 1.966667
   Curtis Blaydes 1.886667
       Sean Sherk 1.858081
   Mateusz Gamrot 1.781667


Top ten fighters for RA_SUB.ATT:
        FIGHTER  RA_SUB.ATT
    John Albert    1.700000
  TJ Waldburger    1.305556
      Paul Sass    1.200000
    James Wilks    1.111111
Preston Parsons    1.000000
     Jason High    0.988889
    Jimmy Flick    0.875000
     Dave Menne    0.853333
  Tatsuro Taira    0.800000
    Jimmy Crute    0.777778


Top ten fighters for RA_REV:
        FIGHTER   RA_REV
Cameron Saaiman 0.694444
   Khama Worthy 0.666667
 Jiri Prochazka 0.650000
     Noad Lahat 0.527778
     Chris Wade 0.514286
 Jerrod Sanders 0.500000
Yosdenis Cedeno 0.500000
 Alvin Robinson 0.500000
   Chase Hooper 0.483333
  Justin Jaynes 0.466667


Top ten fighters for RA_CTRL:
           FIGHTER  RA_CTRL
   Jailton Almeida 3.737937
  Bartosz Fabinski 3.549074
  Gregor Gillespie 3.541250
      Chael Sonnen 3.321349
Rinat Fakhretdinov 3.225463
    Tatiana Suarez 3.023016
     Jacob Malkoun 2.949074
   Philip De Fries 2.912778
 Umar Nurmagomedov 2.885556
       Joe Solecki 2.836508


Top ten fighters for RA_HEAD:
         FIGHTER   RA_HEAD
   Sean O'Malley 31.758333
   Casey O'Neill 30.166667
 Sean Strickland 26.354603
   Frank Camacho 26.077778
    Max Holloway 25.740825
 Khamzat Chimaev 25.333333
Daniel Rodriguez 25.111111
      Ryan Janes 24.305556
  Danaa Batgerel 24.000000
   Parker Porter 23.571429


Top ten fighters for RA_BODY:
         FIGHTER   RA_BODY
     Paulo Costa 11.247222
Anthony Hamilton 10.700000
      Kai Kamaka 10.333333
     Tony Kelley  9.944444
      CJ Vergara  9.861111
  Michel Pereira  9.833333
   Sean O'Malley  9.583333
    John Lineker  9.516923
      Gabe Green  9.316667
 Fabio Maldonado  9.171970


Top ten fighters for RA_LEG:
           FIGHTER    RA_LEG
 Marcelo Guimaraes 12.833333
   Armen Petrosyan 12.400000
       Joanne Wood 10.921569
    Nathaniel Wood 10.174603
Joanna Jedrzejczyk  9.332143
   Marcin Prachnio  9.148148
         Ken Stone  8.916667
   Chris Gutierrez  8.912121
        Karol Rosa  8.777778
    Khaos Williams  8.619048


Top ten fighters for RA_DISTANCE:
           FIGHTER  RA_DISTANCE
     Sean O'Malley    42.091667
  Daniel Rodriguez    34.331746
      Max Holloway    33.709007
      Emily Ducote    33.333333
     Casey O'Neill    31.833333
         Ian Garry    30.916667
     Mounir Lazzez    30.916667
Ignacio Bahamondes    30.200000
     Frank Camacho    29.750000
      Pedro Munhoz    29.689963


Top ten fighters for RA_CLINCH:
             FIGHTER  RA_CLINCH
        Josh Barnett  13.937143
       Sarah Kaufman  12.944444
       Lavar Johnson   9.166667
         Tony Kelley   8.500000
   Shavkat Rakhmonov   8.416667
  Isaac Vallie-Flagg   7.916667
Marc-Andre Barriault   7.700000
     Yana Kunitskaya   7.695238
    Loma Lookboonmee   7.684524
     Timothy Johnson   7.666667


Top ten fighters for RA_GROUND:
          FIGHTER  RA_GROUND
Umar Nurmagomedov  14.733333
     Brock Lesnar  12.416667
      Danny Henry  12.166667
   Tatiana Suarez  11.907937
    Tatsuro Taira  10.950000
    Luke Rockhold  10.890693
 Saparbeg Safarov  10.583333
 Gregor Gillespie  10.108333
      Mike Guymon  10.083333
       Matt Grice  10.015873


Top ten fighters for RA_SIG:
         FIGHTER    RA_SIG
   Sean O'Malley 43.975000
   Casey O'Neill 38.555556
    Max Holloway 38.131818
Daniel Rodriguez 35.611111
     Joanne Wood 35.235294
   Mounir Lazzez 34.916667
   Sarah Kaufman 34.722222
    Emily Ducote 34.666667
    Shane Burgos 33.763636
       Ian Garry 33.750000


Top ten fighters for RA_STR:
          FIGHTER    RA_STR
    Casey O'Neill 57.111111
    Lavar Johnson 55.083333
    Sarah Kaufman 53.833333
Billy Quarantillo 51.314815
    Pascal Krauss 50.750000
Merab Dvalishvili 50.633333
      Joanne Wood 50.433155
     Jared Gordon 50.136508
       Karol Rosa 50.037037
  Yana Kunitskaya 49.742857


Top ten fighters for RA_TD_DEF:
             FIGHTER  RA_TD_DEF
    Mike de la Torre   3.928571
     Charles Johnson   3.031746
      Shane Campbell   2.822222
            Petr Yan   2.793636
         Diego Nunes   2.722222
      Taylor Lapilus   2.711111
       Adam Milstead   2.666667
Jack Della Maddalena   2.523810
        Austin Lingo   2.500000
          Din Thomas   2.455026


Top ten fighters for RA_HEAD_DEF:
         FIGHTER  RA_HEAD_DEF
        Nam Phan    54.416667
    Emily Ducote    48.416667
        Joe Soto    42.625000
     Syuri Kondo    42.361111
Nasrat Haqparast    41.716667
    Vanessa Melo    41.333333
    Pedro Munhoz    40.383004
   Giga Chikadze    40.016667
 Gloria de Paula    39.944444
      Geoff Neal    39.172294


Top ten fighters for RA_BODY_DEF:
              FIGHTER  RA_BODY_DEF
       Luana Pinheiro     5.750000
           Trey Ogden     5.750000
Karolina Kowalkiewicz     5.124679
      Sean Strickland     4.827302
     Alptekin Ozkilic     4.694444
           Manel Kape     4.666667
             Nam Phan     4.458333
          Randy Costa     4.333333
          Ji Yeon Kim     4.233333
     Daniel Zellhuber     4.138889


Top ten fighters for RA_LEG_DEF:
              FIGHTER  RA_LEG_DEF
       Luana Pinheiro    4.055556
         Hyun Gyu Lim    2.978571
          Phil Harris    2.977778
     Antonio Carvalho    2.750000
Karolina Kowalkiewicz    2.415705
        Johnny Walker    2.366667
  Andreas Michailidis    2.277778
         Hannah Goldy    2.200000
         Alatengheili    2.182540
 Valentina Shevchenko    2.173016


Top ten fighters for RA_DISTANCE_DEF:
         FIGHTER  RA_DISTANCE_DEF
        Nam Phan        57.750000
    Emily Ducote        51.500000
Nasrat Haqparast        44.366667
     Syuri Kondo        44.166667
    Pedro Munhoz        43.562637
        Joe Soto        42.975000
 Gloria de Paula        42.777778
   Giga Chikadze        42.750000
    Vanessa Melo        42.666667
   John Makdessi        42.027778


Top ten fighters for RA_CLINCH_DEF:
         FIGHTER  RA_CLINCH_DEF
 Wesley Correira       5.291667
Stanislav Nedkov       5.166667
     Scott Smith       4.888889
   Alex Chambers       4.511111
     Josh Grispi       4.305556
    Andre Winner       3.944444
 Jonathan Wilson       3.916667
      Vik Grujic       3.833333
  Rashad Coulter       3.777778
    Jorge Rivera       3.677778


Top ten fighters for RA_GROUND_DEF:
              FIGHTER  RA_GROUND_DEF
        Elvis Sinosic      11.888889
          Tim Credeur       9.200000
           Jason Dent       9.000000
Gadzhimurad Antigulov       8.000000
         Terrion Ware       6.250000
         Kyle Bradley       6.250000
       Louis Gaudinot       6.111111
          Jenel Lausa       5.277778
          David Heath       5.033333
       Aaron Phillips       4.972222


Top ten fighters for RA_SIG_DEF:
         FIGHTER  RA_SIG_DEF
        Nam Phan   59.666667
    Emily Ducote   52.083333
     Syuri Kondo   46.750000
Nasrat Haqparast   44.774074
        Joe Soto   44.600000
    Vanessa Melo   44.416667
    Pedro Munhoz   44.059780
Alptekin Ozkilic   43.472222
   Giga Chikadze   43.191667
 Gloria de Paula   43.111111


Top ten fighters for RA_STR_DEF:
           FIGHTER  RA_STR_DEF
          Nam Phan   61.958333
      Emily Ducote   52.500000
       Syuri Kondo   49.305556
   Gloria de Paula   45.972222
      Vanessa Melo   45.416667
  Nasrat Haqparast   45.392593
          Joe Soto   45.075000
      Pedro Munhoz   44.141832
  Alptekin Ozkilic   43.916667
Valerie Letourneau   43.700000


Top ten fighters for RA_TD_%:
         FIGHTER  RA_TD_%
   Johnny Walker      1.0
      Sumudaerji      1.0
    Jared Gooden      1.0
      Uros Medic      1.0
    Alex Pereira      1.0
    Tom Aspinall      1.0
    Andre Fialho      1.0
    Omar Morales      1.0
Cheyanne Vlismas      1.0
  Mariya Agapova      1.0


Top ten fighters for RA_HEAD_%:
          FIGHTER  RA_HEAD_%
   Danilo Marques   0.754630
     Brock Lesnar   0.694915
      Mike Guymon   0.693467
  Cyrille Diabate   0.692884
Umar Nurmagomedov   0.687320
 Alistair Overeem   0.678372
  Jailton Almeida   0.661527
    Tatsuro Taira   0.654852
  Junior Assuncao   0.632317
   Darrick Minner   0.609272


Top ten fighters for RA_BODY_%:
          FIGHTER  RA_BODY_%
   Jeremy Kimball        1.0
   Jerrod Sanders        1.0
        Paul Sass        1.0
        Ken Stone        1.0
     Shane Carwin        1.0
Chris Tuchscherer        1.0
     Jake O'Brien        1.0
   Alvin Robinson        1.0
      Rory Singer        1.0
      Marcio Cruz        1.0


Top ten fighters for RA_LEG_%:
         FIGHTER  RA_LEG_%
   Nikolas Motta       1.0
    Martin Buday       1.0
    Steve Garcia       1.0
     Joe Solecki       1.0
      Uros Medic       1.0
Sergei Pavlovich       1.0
    Tom Aspinall       1.0
     Nate Maness       1.0
   Jacob Malkoun       1.0
     Zarah Fairn       1.0


Top ten fighters for RA_DISTANCE_%:
         FIGHTER  RA_DISTANCE_%
 Cyrille Diabate       0.711375
  Danilo Marques       0.681564
Alistair Overeem       0.655143
   Tatsuro Taira       0.645985
  Justin Gaethje       0.639857
    Tom Aspinall       0.632530
   Sean O'Malley       0.630429
    Alex Pereira       0.611241
    Sean Soriano       0.611033
  Darrick Minner       0.599138


Top ten fighters for RA_CLINCH_%:
               FIGHTER  RA_CLINCH_%
     Umar Nurmagomedov          1.0
             Joe Pyfer          1.0
           Mike Malott          1.0
        Yohan Lainesse          1.0
          Rafael Alves          1.0
      Michael Chandler          1.0
          Vanessa Melo          1.0
     Chance Rencountre          1.0
Alvaro Herrera Mendoza          1.0
            Dongi Yang          1.0


Top ten fighters for RA_GROUND_%:
           FIGHTER  RA_GROUND_%
   Marcin Prachnio          1.0
         Zac Pauga          1.0
    Yohan Lainesse          1.0
  Carlos Hernandez          1.0
        Trey Ogden          1.0
Modestas Bukauskas          1.0
     Hakeem Dawodu          1.0
           JP Buys          1.0
      Aleksa Camur          1.0
    Journey Newson          1.0


Top ten fighters for RA_SIG_%:
          FIGHTER  RA_SIG_%
   Danilo Marques  0.795222
  Cyrille Diabate  0.753886
     Brock Lesnar  0.752034
      Mike Guymon  0.750000
 Alistair Overeem  0.747861
    Tatsuro Taira  0.708705
Umar Nurmagomedov  0.701841
 Bartosz Fabinski  0.698113
   Darrick Minner  0.687943
     Josh Barnett  0.687268


Top ten fighters for RA_STR_%:
          FIGHTER  RA_STR_%
      Mike Guymon  0.873727
     Brock Lesnar  0.849720
   Darrick Minner  0.805024
 Bartosz Fabinski  0.803333
 Magnus Cedenblad  0.801223
   Aaron Phillips  0.800000
  Cyrille Diabate  0.791878
     Pat Sabatini  0.788863
Charlie Brenneman  0.785720
   Eugene Jackson  0.782178


Top ten fighters for RA_TD_DEF_%:
          FIGHTER  RA_TD_DEF_%
  Jailton Almeida          1.0
     Philipe Lins          1.0
Umar Nurmagomedov          1.0
  Yazmin Jauregui          1.0
Gregory Rodrigues          1.0
   Rodolfo Vieira          1.0
        Zac Pauga          1.0
     Martin Buday          1.0
Shavkat Rakhmonov          1.0
       Trey Ogden          1.0


Top ten fighters for RA_HEAD_DEF_%:
          FIGHTER  RA_HEAD_DEF_%
       Jon Madsen       0.913177
Umar Nurmagomedov       0.890226
    Miguel Torres       0.866083
  Hector Sandoval       0.847826
     Tom Aspinall       0.840000
        Nate Mohr       0.830472
   Claude Patrick       0.828080
Georges St-Pierre       0.816660
   Montel Jackson       0.815615
  Adriano Martins       0.813461


Top ten fighters for RA_BODY_DEF_%:
        FIGHTER  RA_BODY_DEF_%
     Jason Witt       1.000000
 Ivan Salaverry       0.696970
   Hunter Azure       0.660920
    Mike Guymon       0.642857
    Phil Harris       0.627986
Anthony Birchak       0.626667
    Frank Trigg       0.625000
 Luana Pinheiro       0.612426
  Kajan Johnson       0.612022
      Nate Mohr       0.606061


Top ten fighters for RA_LEG_DEF_%:
        FIGHTER  RA_LEG_DEF_%
   Chael Sonnen      0.616000
     Jason Witt      0.600000
 Jordan Leavitt      0.583784
 Tatiana Suarez      0.576052
    Vaughan Lee      0.566802
    Gina Mazany      0.550964
Muhammad Mokaev      0.548611
  Lavar Johnson      0.533333
 Naoyuki Kotani      0.529412
Anthony Birchak      0.520000


Top ten fighters for RA_DISTANCE_DEF_%:
        FIGHTER  RA_DISTANCE_DEF_%
     Jon Madsen           0.859485
 Ivan Salaverry           0.839408
 Claude Patrick           0.836512
Jailton Almeida           0.832512
Kevin Randleman           0.831250
    Rory Singer           0.825688
    Frank Trigg           0.822430
  Miguel Torres           0.817469
    Marcio Cruz           0.814607
    Mike Guymon           0.797468


Top ten fighters for RA_CLINCH_DEF_%:
       FIGHTER  RA_CLINCH_DEF_%
    Trey Ogden         1.000000
   Ryan Jensen         0.857143
   Tim Credeur         0.842105
  Adrian Yanez         0.826087
 Chris Dempsey         0.823529
  Brian Bowles         0.804878
   Mike Malott         0.750000
    Greg Hardy         0.727273
    Ben Nguyen         0.707317
Jason Gonzalez         0.687500


Top ten fighters for RA_GROUND_DEF_%:
          FIGHTER  RA_GROUND_DEF_%
 Daniel Zellhuber         1.000000
  Michael Morales         1.000000
       Trey Ogden         1.000000
     Tom Aspinall         1.000000
 Rashid Magomedov         1.000000
Victoria Leonardo         0.766667
   Sergey Morozov         0.750000
 Darren Uyenoyama         0.727273
    Fredy Serrano         0.703704
 Magomed Ankalaev         0.685714


Top ten fighters for RA_SIG_DEF_%:
          FIGHTER  RA_SIG_DEF_%
       Jon Madsen      0.820038
Umar Nurmagomedov      0.778037
    Miguel Torres      0.763029
        Nate Mohr      0.756364
Georges St-Pierre      0.742544
   Ivan Salaverry      0.738488
Cristiane Justino      0.728064
     Justin Salas      0.720828
  Danielle Taylor      0.716895
   Aiemann Zahabi      0.713749


Top ten fighters for RA_STR_DEF_%:
          FIGHTER  RA_STR_DEF_%
       Jon Madsen      0.770283
        Nate Mohr      0.724437
   Aiemann Zahabi      0.703638
  Danielle Taylor      0.700000
     Justin Salas      0.693082
Cristiane Justino      0.690855
  Vinny Magalhaes      0.679944
      Miles Johns      0.679494
 Cynthia Calvillo      0.678578
      Phil Harris      0.677100


ra_champions = ra[ra['EITHER_CHAMP'] == 'Yes']

ra_champions


# Calculate the mean for each of the specified columns
means1 = ra_champions[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means1.plot(kind='bar', color='palegreen')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Calculate the mean for each of the specified columns
meansA = ra_champions[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansA = meansA.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansA.plot(kind='bar', color='hotpink')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


ra_challengers = ra[ra['EVER_CHALLENGER'] == 'Yes']

ra_challengers


# Calculate the mean for each of the specified columns
means2 = ra_challengers[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means2.plot(kind='bar', color='goldenrod')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Calculate the mean for each of the specified columns
meansB = ra_challengers[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansB = meansB.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansB.plot(kind='bar', color='deeppink')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


ra_non_champions = ra[ra['EITHER_CHAMP'] != 'Yes']

ra_non_champions


# Calculate the mean for each of the specified columns
means3 = ra_non_champions[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means3.plot(kind='bar', color='blueviolet')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Calculate the mean for each of the specified columns
meansC = ra_non_champions[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansC = meansC.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansC.plot(kind='bar', color='mediumvioletred')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


ra_non_challengers = ra[ra['EVER_CHALLENGER'] != 'Yes']

ra_non_challengers


# Calculate the mean for each of the specified columns
means4 = ra_non_challengers[metrics1].mean()

# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_means4.plot(kind='bar', color='darkorange')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  # Rotate x-axis labels for better visibility
plt.tight_layout()
plt.show()


# Calculate the mean for each of the specified columns
meansD = ra_non_challengers[metrics2].mean()

# Rank the means from highest to lowest and plot again
sorted_meansD = meansD.sort_values(ascending=False)


# Create a bar chart
plt.figure(figsize=(14, 7))
sorted_meansD.plot(kind='bar', color='lightcoral')
plt.title('Means Across Different Metrics')
plt.xlabel('Metrics')
plt.ylabel('Mean Percentile')
plt.xticks(rotation=90)  
plt.show()


# # Display max rows
# pd.set_option('display.max_rows', None)

# breakdown(records)
# # Set the display options to show 20 rows at the top and bottom
# pd.set_option('display.max_rows', 20)


# Filter the dataframe using the correct column name "FIGHTER"
r = records[records['FIGHTER'].isin(fighters_list)]

r.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1321 entries, 1 to 2286
Data columns (total 46 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   FIGHTER                        1321 non-null   object 
 1   STANCE                         1302 non-null   object 
 2   EVER_CHAMPION                  1321 non-null   object 
 3   EVER_INTERIM                   1321 non-null   object 
 4   EVER_CHALLENGER                1321 non-null   object 
 5   EITHER_CHAMP                   1321 non-null   object 
 6   Total_Rounds                   1321 non-null   int64  
 7   Total_Fights                   1321 non-null   int64  
 8   Wins                           1321 non-null   int64  
 9   Losses                         1321 non-null   int64  
 10  Draws                          1321 non-null   int64  
 11  No_Contests                    1321 non-null   int64  
 12  Win_by_KO_TKO                  1321 non-null   int64  
 13  Win_by_Submission              1321 non-null   int64  
 14  Win_by_Decision                1321 non-null   int64  
 15  Loss_by_KO_TKO                 1321 non-null   int64  
 16  Loss_by_Submission             1321 non-null   int64  
 17  Loss_by_Decision               1321 non-null   int64  
 18  Total_Fights_with_Title        1321 non-null   float64
 19  Wins_with_Title                1321 non-null   float64
 20  Losses_with_Title              1321 non-null   float64
 21  Draws_with_Title               1321 non-null   float64
 22  No_Contests_with_Title         1321 non-null   float64
 23  Win_by_KO_TKO_with_Title       1321 non-null   float64
 24  Win_by_Submission_with_Title   1321 non-null   float64
 25  Win_by_Decision_with_Title     1321 non-null   float64
 26  Loss_by_KO_TKO_with_Title      1321 non-null   float64
 27  Loss_by_Submission_with_Title  1321 non-null   float64
 28  Loss_by_Decision_with_Title    1321 non-null   float64
 29  Win_%                          1321 non-null   float64
 30  Loss_%                         1321 non-null   float64
 31  KO_Win_%                       1321 non-null   float64
 32  Sub_Win_%                      1321 non-null   float64
 33  Dec_Win_%                      1321 non-null   float64
 34  KO_Loss_%                      1321 non-null   float64
 35  Sub_Loss_%                     1321 non-null   float64
 36  Dec_Loss_%                     1321 non-null   float64
 37  Title_Win_%                    213 non-null    float64
 38  Title_Loss_%                   213 non-null    float64
 39  Title_KO_Win_%                 213 non-null    float64
 40  Title_Sub_Win_%                213 non-null    float64
 41  Title_Dec_Win_%                213 non-null    float64
 42  Title_KO_Loss_%                213 non-null    float64
 43  Title_Sub_Loss_%               213 non-null    float64
 44  Title_Dec_Loss_%               213 non-null    float64
 45  Rounds_per_Fight               1321 non-null   float64
dtypes: float64(28), int64(12), object(6)
memory usage: 485.1+ KB


r.describe()


metrics1 = ['Total_Rounds',	'Total_Fights',	'Wins',	'Losses', 'Draws',	'No_Contests',	'Win_by_KO_TKO',
            'Win_by_Submission', 'Win_by_Decision',	'Loss_by_KO_TKO', 'Loss_by_Submission',	'Loss_by_Decision',
            'Total_Fights_with_Title',	'Wins_with_Title',	'Losses_with_Title', 'Draws_with_Title',	'No_Contests_with_Title',	
            'Win_by_KO_TKO_with_Title',	'Win_by_Submission_with_Title',	
            'Win_by_Decision_with_Title',	'Loss_by_KO_TKO_with_Title', 'Loss_by_Submission_with_Title',	
            'Loss_by_Decision_with_Title', 'Rounds_per_Fight']


metrics2 = ['Win_%', 'Loss_%', 'KO_Win_%',	'Sub_Win_%', 'Dec_Win_%',
'KO_Loss_%', 'Sub_Loss_%', 'Dec_Loss_%', 'Title_Win_%',	'Title_Loss_%',	
'Title_KO_Win_%', 'Title_Sub_Win_%', 'Title_Dec_Win_%',	'Title_KO_Loss_%','Title_Sub_Loss_%',	
'Title_Dec_Loss_%']


# Original metrics list
metrics = ['Total_Rounds',	'Total_Fights',	'Wins',	'Losses', 'Draws',	'No_Contests',	'Win_by_KO_TKO',
            'Win_by_Submission', 'Win_by_Decision',	'Loss_by_KO_TKO', 'Loss_by_Submission',	'Loss_by_Decision',
            'Total_Fights_with_Title',	'Wins_with_Title',	'Losses_with_Title', 'Draws_with_Title',	'No_Contests_with_Title',	
            'Win_by_KO_TKO_with_Title',	'Win_by_Submission_with_Title',	
            'Win_by_Decision_with_Title',	'Loss_by_KO_TKO_with_Title', 'Loss_by_Submission_with_Title',	
            'Loss_by_Decision_with_Title', 'Rounds_per_Fight', 'Win_%', 'Loss_%', 'KO_Win_%',	'Sub_Win_%', 'Dec_Win_%',
'KO_Loss_%', 'Sub_Loss_%', 'Dec_Loss_%', 'Title_Win_%',	'Title_Loss_%',	
'Title_KO_Win_%', 'Title_Sub_Win_%', 'Title_Dec_Win_%',	'Title_KO_Loss_%','Title_Sub_Loss_%',	
'Title_Dec_Loss_%']



# Reinitializing the dictionary to hold the top ten fighters for the updated list of metrics
top_fighters_per_metric = {}

# Creating a dictionary to hold the top ten fighters for each metric
top_fighters_per_metric = {}

# Finding the top ten fighters for each metric
for metric in metrics:
    # Some metrics may have missing values, so we sort only non-NA values
    top_fighters_per_metric[metric] = r.nlargest(10, metric)[['FIGHTER', metric]]

# Printing the top ten fighters for each metric with a blank line in between
for metric, data in top_fighters_per_metric.items():
    print(f"Top ten fighters for {metric}:")
    if isinstance(data, pd.DataFrame):
        print(data.to_string(index=False))
    else:
        print(data)
    print("\n")  # Blank line for separation

Top ten fighters for Total_Rounds:
         FIGHTER  Total_Rounds
Rafael Dos Anjos           111
   Frankie Edgar            99
      Jim Miller            96
 Andrei Arlovski            92
      Clay Guida            92
    Max Holloway            90
     Demian Maia            89
      Neil Magny            87
 Jeremy Stephens            86
   Diego Sanchez            84


Top ten fighters for Total_Fights:
         FIGHTER  Total_Fights
      Jim Miller            43
 Andrei Arlovski            41
  Donald Cerrone            38
      Clay Guida            36
Rafael Dos Anjos            35
 Jeremy Stephens            34
     Demian Maia            33
      Neil Magny            32
Charles Oliveira            32
   Diego Sanchez            32


Top ten fighters for Wins:
         FIGHTER  Wins
      Jim Miller    26
 Andrei Arlovski    23
  Donald Cerrone    23
  Dustin Poirier    22
      Neil Magny    22
Charles Oliveira    22
     Demian Maia    22
Rafael Dos Anjos    21
       Jon Jones    21
    Max Holloway    20


Top ten fighters for Losses:
         FIGHTER  Losses
      Clay Guida      18
 Jeremy Stephens      18
 Andrei Arlovski      17
      Jim Miller      16
 Michael Johnson      15
Rafael Dos Anjos      14
  Donald Cerrone      14
     Angela Hill      13
      Matt Brown      13
       Sam Alvey      13


Top ten fighters for Draws:
         FIGHTER  Draws
  Brandon Moreno      2
Marina Rodriguez      2
   Lando Vannata      2
         BJ Penn      2
        Caol Uno      2
    Ion Cutelaba      1
     Song Yadong      1
   Ludovit Klein      1
Charles Jourdain      1
    Sean Woodson      1


Top ten fighters for No_Contests:
            FIGHTER  No_Contests
       Pedro Munhoz            2
         Niko Price            2
     Darren Stewart            2
        Kevin Casey            2
       Thiago Silva            2
     Matthew Riddle            2
     Curtis Blaydes            1
     Dustin Poirier            1
      Kevin Holland            1
Michal Oleksiejczuk            1


Top ten fighters for Win_by_KO_TKO:
          FIGHTER  Win_by_KO_TKO
    Derrick Lewis             14
       Matt Brown             13
   Dustin Poirier             11
    Thiago Santos             11
   Anderson Silva             11
  Anthony Johnson             11
   Donald Cerrone             10
  Francis Ngannou             10
Junior Dos Santos             10
   Cain Velasquez             10


Top ten fighters for Win_by_Submission:
           FIGHTER  Win_by_Submission
  Charles Oliveira                 16
        Jim Miller                 12
       Demian Maia                 11
         Nate Diaz                 10
Gerald Meerschaert                  9
     Gunnar Nelson                  8
         Frank Mir                  8
 Gillian Robertson                  7
     Brendan Allen                  7
        Rani Yahya                  7


Top ten fighters for Win_by_Decision:
          FIGHTER  Win_by_Decision
       Neil Magny               14
     Brad Tavares               13
 Rafael Dos Anjos               12
  Andrei Arlovski               12
    Diego Sanchez               12
Georges St-Pierre               12
 Robert Whittaker               11
       Clay Guida               11
    Darren Elkins               11
   Belal Muhammad               11


Top ten fighters for Loss_by_KO_TKO:
          FIGHTER  Loss_by_KO_TKO
  Andrei Arlovski               8
   Donald Cerrone               8
 Alistair Overeem               8
    Stefan Struve               8
  Gabriel Gonzaga               8
        Frank Mir               8
    Derrick Lewis               7
Junior Dos Santos               7
     Ben Saunders               7
 Jake Ellenberger               7


Top ten fighters for Loss_by_Submission:
               FIGHTER  Loss_by_Submission
            Clay Guida                   7
       Melvin Guillard                   6
           Joanne Wood                   5
Marcos Rogerio de Lima                   5
       Michael Johnson                   5
            Neil Magny                   5
             Tim Means                   5
           Cub Swanson                   5
        Michael Chiesa                   5
          Alex Caceres                   5


Top ten fighters for Loss_by_Decision:
         FIGHTER  Loss_by_Decision
 Jeremy Stephens                12
      Jim Miller                11
     Angela Hill                11
Rafael Dos Anjos                10
    Pedro Munhoz                 9
      Clay Guida                 9
  Jorge Masvidal                 9
       Nate Diaz                 9
     Jessica Eye                 9
     Demian Maia                 9


Top ten fighters for Total_Fights_with_Title:
             FIGHTER  Total_Fights_with_Title
           Jon Jones                     16.0
   Georges St-Pierre                     15.0
  Demetrious Johnson                     14.0
       Randy Couture                     14.0
      Anderson Silva                     13.0
        Amanda Nunes                     12.0
           Jose Aldo                     12.0
         Matt Hughes                     12.0
Valentina Shevchenko                     11.0
     Israel Adesanya                     11.0


Top ten fighters for Wins_with_Title:
             FIGHTER  Wins_with_Title
           Jon Jones             15.0
   Georges St-Pierre             13.0
  Demetrious Johnson             12.0
        Amanda Nunes             11.0
      Anderson Silva             11.0
         Matt Hughes              9.0
Valentina Shevchenko              8.0
     Israel Adesanya              8.0
           Jose Aldo              8.0
       Randy Couture              8.0


Top ten fighters for Losses_with_Title:
           FIGHTER  Losses_with_Title
     Randy Couture                6.0
     Frankie Edgar                5.0
           BJ Penn                5.0
        Holly Holm                4.0
      Max Holloway                4.0
         Jose Aldo                4.0
Joanna Jedrzejczyk                4.0
      Urijah Faber                4.0
     Vitor Belfort                4.0
        Tim Sylvia                4.0


Top ten fighters for Draws_with_Title:
             FIGHTER  Draws_with_Title
      Brandon Moreno               1.0
    Magomed Ankalaev               1.0
    Stephen Thompson               1.0
 Deiveson Figueiredo               1.0
        Alexa Grasso               1.0
Valentina Shevchenko               1.0
      Jan Blachowicz               1.0
       Frankie Edgar               1.0
       Tyron Woodley               1.0
             BJ Penn               1.0


Top ten fighters for No_Contests_with_Title:
             FIGHTER  No_Contests_with_Title
           Jon Jones                     1.0
      Daniel Cormier                     1.0
          CJ Vergara                     0.0
      Curtis Blaydes                     0.0
     Jailton Almeida                     0.0
  Benoit Saint Denis                     0.0
      Dustin Poirier                     0.0
       Gilbert Burns                     0.0
Jack Della Maddalena                     0.0
        Ion Cutelaba                     0.0


Top ten fighters for Win_by_KO_TKO_with_Title:
             FIGHTER  Win_by_KO_TKO_with_Title
      Anderson Silva                       7.0
        TJ Dillashaw                       5.0
         Matt Hughes                       5.0
       Randy Couture                       5.0
       Chuck Liddell                       5.0
Valentina Shevchenko                       4.0
        Amanda Nunes                       4.0
        Max Holloway                       4.0
        Stipe Miocic                       4.0
        Henry Cejudo                       3.0


Top ten fighters for Win_by_Submission_with_Title:
            FIGHTER  Win_by_Submission_with_Title
 Demetrious Johnson                           5.0
          Jon Jones                           4.0
Khabib Nurmagomedov                           3.0
     Daniel Cormier                           3.0
            BJ Penn                           3.0
       Ronda Rousey                           3.0
        Matt Hughes                           3.0
Deiveson Figueiredo                           2.0
       Amanda Nunes                           2.0
     Anthony Pettis                           2.0


Top ten fighters for Win_by_Decision_with_Title:
              FIGHTER  Win_by_Decision_with_Title
            Jon Jones                         8.0
    Georges St-Pierre                         8.0
            Jose Aldo                         6.0
      Israel Adesanya                         5.0
         Amanda Nunes                         5.0
   Demetrious Johnson                         5.0
Alexander Volkanovski                         4.0
 Valentina Shevchenko                         4.0
        Dominick Cruz                         4.0
   Joanna Jedrzejczyk                         4.0


Top ten fighters for Loss_by_KO_TKO_with_Title:
        FIGHTER  Loss_by_KO_TKO_with_Title
  Randy Couture                        6.0
      Jose Aldo                        4.0
  Vitor Belfort                        3.0
      Frank Mir                        3.0
 Cody Garbrandt                        2.0
Jessica Andrade                        2.0
   TJ Dillashaw                        2.0
  Luke Rockhold                        2.0
 Chan Sung Jung                        2.0
   Stipe Miocic                        2.0


Top ten fighters for Loss_by_Submission_with_Title:
            FIGHTER  Loss_by_Submission_with_Title
         Tim Sylvia                            3.0
     Dustin Poirier                            2.0
        Miesha Tate                            2.0
    Anthony Johnson                            2.0
        Matt Hughes                            2.0
        Frank Trigg                            2.0
         Alex Perez                            1.0
        Josh Emmett                            1.0
Deiveson Figueiredo                            1.0
      Derrick Lewis                            1.0


Top ten fighters for Loss_by_Decision_with_Title:
           FIGHTER  Loss_by_Decision_with_Title
     Frankie Edgar                          5.0
      Max Holloway                          4.0
Joanna Jedrzejczyk                          3.0
      Urijah Faber                          3.0
           BJ Penn                          3.0
    Brandon Moreno                          2.0
   Colby Covington                          2.0
   Israel Adesanya                          2.0
        Holly Holm                          2.0
   Glover Teixeira                          2.0


Top ten fighters for Win_%:
             FIGHTER  Win_%
Jack Della Maddalena  100.0
     Muhammad Mokaev  100.0
   Umar Nurmagomedov  100.0
       Vitor Petrino  100.0
        Ilia Topuria  100.0
       Natalia Silva  100.0
       Movsar Evloev  100.0
   Dricus Du Plessis  100.0
   Shavkat Rakhmonov  100.0
      Paddy Pimblett  100.0


Top ten fighters for Loss_%:
         FIGHTER  Loss_%
     Zarah Fairn   100.0
         JP Buys   100.0
    Istela Nunes   100.0
  Aaron Phillips   100.0
  Alen Amedovski   100.0
     Alan Baudot   100.0
    Sean Soriano   100.0
   Jerome Rivera   100.0
      Martin Day   100.0
Vinicius Moreira   100.0


Top ten fighters for KO_Win_%:
          FIGHTER  KO_Win_%
 Sergei Pavlovich 75.000000
   Danaa Batgerel 75.000000
    Gerald Harris 75.000000
  Francis Ngannou 71.428571
  Ricco Rodriguez 71.428571
     Steve Garcia 66.666667
   Cain Velasquez 66.666667
     Shane Carwin 66.666667
Gregory Rodrigues 62.500000
     Tom Aspinall 62.500000


Top ten fighters for Sub_Win_%:
          FIGHTER  Sub_Win_%
Shavkat Rakhmonov  83.333333
   Rodolfo Vieira  71.428571
  Muhammad Mokaev  60.000000
 Erin Blanchfield  60.000000
   Manny Bermudez  60.000000
        Paul Sass  60.000000
   Tatiana Suarez  57.142857
    Brendan Allen  53.846154
    Gunnar Nelson  53.333333
Anthony Hernandez  50.000000


Top ten fighters for Dec_Win_%:
            FIGHTER  Dec_Win_%
      Movsar Evloev      100.0
       Tracy Cortez      100.0
      Caio Borralho       80.0
     Javid Basharat       75.0
  Merab Dvalishvili       75.0
Waldo Cortes-Acosta       75.0
       Norma Dumont       75.0
      Arjan Bhullar       75.0
     Jordan Johnson       75.0
     Jeremy Kennedy       75.0


Top ten fighters for KO_Loss_%:
          FIGHTER  KO_Loss_%
   Ike Villanueva  83.333333
   Marvin Eastman  80.000000
     Ken Shamrock  80.000000
  Claudio Ribeiro  75.000000
          JP Buys  75.000000
Victoria Leonardo  75.000000
      Alan Baudot  75.000000
 Vinicius Moreira  75.000000
     Charles Byrd  75.000000
Chris de la Rocha  75.000000


Top ten fighters for Sub_Loss_%:
        FIGHTER  Sub_Loss_%
    John Albert   80.000000
  Tom Niinimaki   75.000000
    Mike Guymon   75.000000
 Emily Whitmire   66.666667
 Brandon Thatch   66.666667
    Ryan Jensen   62.500000
   Sean Soriano   60.000000
  Alex Chambers   60.000000
Shannon Gugerty   60.000000
    Pete Spratt   57.142857


Top ten fighters for Dec_Loss_%:
          FIGHTER  Dec_Loss_%
   Jodie Esquibel  100.000000
     Terrion Ware  100.000000
Michihiro Omigawa   85.714286
   Steve Cantwell   83.333333
   Jordan Griffin   80.000000
     John Alessio   80.000000
     Aleksa Camur   75.000000
   Maria Oliveira   75.000000
   Aaron Phillips   75.000000
       Kay Hansen   75.000000


Top ten fighters for Title_Win_%:
            FIGHTER  Title_Win_%
      Sean O'Malley        100.0
       Ilia Topuria        100.0
  Dricus Du Plessis        100.0
  Alexandre Pantoja        100.0
       Leon Edwards        100.0
       Tom Aspinall        100.0
    Islam Makhachev        100.0
       Jamahal Hill        100.0
Khabib Nurmagomedov        100.0
       Josh Barnett        100.0


Top ten fighters for Title_Loss_%:
          FIGHTER  Title_Loss_%
    Gilbert Burns         100.0
      Marlon Vera         100.0
       Alex Perez         100.0
   Brandon Royval         100.0
     Brian Ortega         100.0
     Amanda Lemos         100.0
      Paulo Costa         100.0
Mayra Bueno Silva         100.0
     Irene Aldana         100.0
      Josh Emmett         100.0


Top ten fighters for Title_KO_Win_%:
       FIGHTER  Title_KO_Win_%
  Ilia Topuria      100.000000
  Tom Aspinall      100.000000
  Josh Barnett      100.000000
Conor McGregor       75.000000
 Chuck Liddell       71.428571
  Alex Pereira       66.666667
  TJ Dillashaw       62.500000
 Chris Weidman       60.000000
Anderson Silva       53.846154
 Sean O'Malley       50.000000


Top ten fighters for Title_Sub_Win_%:
                 FIGHTER  Title_Sub_Win_%
     Khabib Nurmagomedov        75.000000
            Pat Miletich        66.666667
          Yair Rodriguez        50.000000
           Tony Ferguson        50.000000
          Jiri Prochazka        50.000000
            Alexa Grasso        50.000000
         Islam Makhachev        50.000000
           Julianna Pena        50.000000
          Anthony Pettis        50.000000
Antonio Rodrigo Nogueira        50.000000


Top ten fighters for Title_Dec_Win_%:
              FIGHTER  Title_Dec_Win_%
    Dricus Du Plessis       100.000000
    Alexandre Pantoja       100.000000
         Jamahal Hill       100.000000
          Jens Pulver       100.000000
     Benson Henderson        80.000000
         Leon Edwards        66.666667
        Dominick Cruz        66.666667
    Georges St-Pierre        53.333333
        Sean O'Malley        50.000000
Alexander Volkanovski        50.000000


Top ten fighters for Title_KO_Loss_%:
          FIGHTER  Title_KO_Loss_%
    Gilbert Burns            100.0
      Paulo Costa            100.0
 Sergei Pavlovich            100.0
  Volkan Oezdemir            100.0
  Kai Kara-France            100.0
    Jessica Penne            100.0
    Lauren Murphy            100.0
 Michael Chandler            100.0
Katlyn Chookagian            100.0
      Jessica Eye            100.0


Top ten fighters for Title_Sub_Loss_%:
        FIGHTER  Title_Sub_Loss_%
     Alex Perez             100.0
    Josh Emmett             100.0
      Kevin Lee             100.0
    Darren Till             100.0
 Megan Anderson             100.0
       Ray Borg             100.0
    Wilson Reis             100.0
    Cat Zingano             100.0
    John Moraga             100.0
Anthony Johnson             100.0


Top ten fighters for Title_Dec_Loss_%:
              FIGHTER  Title_Dec_Loss_%
          Marlon Vera             100.0
       Brandon Royval             100.0
         Amanda Lemos             100.0
    Mayra Bueno Silva             100.0
         Irene Aldana             100.0
        Anthony Smith             100.0
          Tim Elliott             100.0
      Kelvin Gastelum             100.0
        Jennifer Maia             100.0
Karolina Kowalkiewicz             100.0


Top ten fighters for Rounds_per_Fight:
             FIGHTER  Rounds_per_Fight
  Demetrious Johnson          3.833333
       Dominick Cruz          3.800000
  Joanna Jedrzejczyk          3.800000
Valentina Shevchenko          3.750000
     Israel Adesanya          3.750000
    Benson Henderson          3.714286
        Kamaru Usman          3.705882
     Colby Covington          3.625000
          Holly Holm          3.533333
        Leon Edwards          3.529412


# Filter the dataframe using the correct column name "FIGHTER"
records_1 = records[records['FIGHTER'].isin(top_1_percent_list)]

records_1


records_1.describe()


# Calculate the mean for each of the specified columns
means_1 = records_1[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means_1 = means_1.sort_values(ascending=False)

# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means_1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Total_Rounds                     68.615385
Win_%                            68.082145
Title_Loss_%                     58.822844
Title_Win_%                      40.608974
Loss_%                           29.984389
Dec_Win_%                        28.778303
Total_Fights                     27.692308
Title_KO_Loss_%                  25.641026
KO_Win_%                         24.287578
Title_Dec_Loss_%                 24.242424
Wins                             18.307692
Title_KO_Win_%                   17.054196
Title_Dec_Win_%                  16.853147
Dec_Loss_%                       14.676632
Sub_Win_%                        14.490980
KO_Loss_%                        11.337692
Title_Sub_Loss_%                  8.939394
Losses                            8.846154
Win_by_Decision                   7.538462
Title_Sub_Win_%                   6.701632
Win_by_KO_TKO                     6.307692
Total_Fights_with_Title           4.923077
Loss_by_Decision                  4.615385
Win_by_Submission                 4.307692
Sub_Loss_%                        3.635617
Wins_with_Title                   3.538462
Loss_by_KO_TKO                    3.153846
Rounds_per_Fight                  2.506453
Win_by_Decision_with_Title        1.538462
Win_by_KO_TKO_with_Title          1.307692
Losses_with_Title                 1.307692
Loss_by_Submission                1.000000
Win_by_Submission_with_Title      0.692308
Loss_by_KO_TKO_with_Title         0.615385
No_Contests                       0.461538
Loss_by_Decision_with_Title       0.384615
Loss_by_Submission_with_Title     0.307692
No_Contests_with_Title            0.076923
Draws                             0.076923
Draws_with_Title                  0.000000
dtype: float64


# Filter the dataframe using the correct column name "FIGHTER"
records_10 = records[records['FIGHTER'].isin(top_1_percent_list)]

records_10


records_10.describe()


# Calculate the mean for each of the specified columns
means_10 = records_10[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means_10 = means_10.sort_values(ascending=False)

# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means_10)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Total_Rounds                     68.615385
Win_%                            68.082145
Title_Loss_%                     58.822844
Title_Win_%                      40.608974
Loss_%                           29.984389
Dec_Win_%                        28.778303
Total_Fights                     27.692308
Title_KO_Loss_%                  25.641026
KO_Win_%                         24.287578
Title_Dec_Loss_%                 24.242424
Wins                             18.307692
Title_KO_Win_%                   17.054196
Title_Dec_Win_%                  16.853147
Dec_Loss_%                       14.676632
Sub_Win_%                        14.490980
KO_Loss_%                        11.337692
Title_Sub_Loss_%                  8.939394
Losses                            8.846154
Win_by_Decision                   7.538462
Title_Sub_Win_%                   6.701632
Win_by_KO_TKO                     6.307692
Total_Fights_with_Title           4.923077
Loss_by_Decision                  4.615385
Win_by_Submission                 4.307692
Sub_Loss_%                        3.635617
Wins_with_Title                   3.538462
Loss_by_KO_TKO                    3.153846
Rounds_per_Fight                  2.506453
Win_by_Decision_with_Title        1.538462
Win_by_KO_TKO_with_Title          1.307692
Losses_with_Title                 1.307692
Loss_by_Submission                1.000000
Win_by_Submission_with_Title      0.692308
Loss_by_KO_TKO_with_Title         0.615385
No_Contests                       0.461538
Loss_by_Decision_with_Title       0.384615
Loss_by_Submission_with_Title     0.307692
No_Contests_with_Title            0.076923
Draws                             0.076923
Draws_with_Title                  0.000000
dtype: float64


# Bottom 90 Percent
records_bottom_90 = records[~records['FIGHTER'].isin(top_1_percent_list)]

records_bottom_90


records_bottom_90.describe()


# Calculate the mean for each of the specified columns
means_90 = records_bottom_90[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means_90 = means_90.sort_values(ascending=False)

# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means_90)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Title_Loss_%                     69.809507
Loss_%                           61.847029
Win_%                            36.323768
Title_KO_Loss_%                  29.201299
Title_Win_%                      28.217670
Title_Dec_Loss_%                 27.258795
Dec_Loss_%                       26.656872
KO_Loss_%                        21.405818
Dec_Win_%                        17.380853
Total_Rounds                     14.623264
Sub_Loss_%                       13.572657
Title_Sub_Loss_%                 13.230365
Title_KO_Win_%                   11.886020
KO_Win_%                         11.652643
Title_Dec_Win_%                  10.967653
Sub_Win_%                         7.121023
Total_Fights                      6.171875
Title_Sub_Win_%                   5.268759
Losses                            3.057726
Wins                              3.003472
Rounds_per_Fight                  2.269684
Loss_by_Decision                  1.433160
Win_by_Decision                   1.417101
Loss_by_KO_TKO                    1.013889
Win_by_KO_TKO                     0.994358
Loss_by_Submission                0.601997
Win_by_Submission                 0.583767
Total_Fights_with_Title           0.262153
Losses_with_Title                 0.134549
Wins_with_Title                   0.121962
No_Contests                       0.064236
Loss_by_KO_TKO_with_Title         0.057726
Win_by_KO_TKO_with_Title          0.053819
Loss_by_Decision_with_Title       0.052951
Win_by_Decision_with_Title        0.046441
Draws                             0.046441
Loss_by_Submission_with_Title     0.023438
Win_by_Submission_with_Title      0.021267
Draws_with_Title                  0.005208
No_Contests_with_Title            0.000434
dtype: float64


# Champions
r_champions = r[r['EITHER_CHAMP'] == 'Yes']

r_champions


r_champions.describe()


# Calculate the mean for each of the specified columns
means1 = r_champions[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means1 = means1.sort_values(ascending=False)


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means1)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Win_%                            68.529475
Title_Win_%                      59.926305
Total_Rounds                     44.213592
Title_Loss_%                     38.417939
Loss_%                           29.899001
Dec_Win_%                        28.718281
KO_Win_%                         28.092761
Title_KO_Win_%                   25.569518
Title_Dec_Win_%                  23.190211
Title_KO_Loss_%                  18.990201
Total_Fights                     16.281553
KO_Loss_%                        13.559472
Title_Dec_Loss_%                 12.453208
Dec_Loss_%                       11.983180
Sub_Win_%                        11.631376
Wins                             11.048544
Title_Sub_Win_%                  10.972401
Title_Sub_Loss_%                  6.731812
Total_Fights_with_Title           5.009709
Losses                            4.970874
Win_by_Decision                   4.747573
Win_by_KO_TKO                     4.388350
Sub_Loss_%                        4.239454
Wins_with_Title                   3.145631
Rounds_per_Fight                  2.694256
Loss_by_Decision                  2.126214
Loss_by_KO_TKO                    2.126214
Win_by_Submission                 1.893204
Losses_with_Title                 1.766990
Win_by_KO_TKO_with_Title          1.359223
Win_by_Decision_with_Title        1.223301
Loss_by_KO_TKO_with_Title         0.844660
Loss_by_Submission                0.699029
Loss_by_Decision_with_Title       0.621359
Win_by_Submission_with_Title      0.553398
Loss_by_Submission_with_Title     0.291262
Draws                             0.135922
No_Contests                       0.126214
Draws_with_Title                  0.077670
No_Contests_with_Title            0.019417
dtype: float64


# Challengers
r_challengers = r[r['EVER_CHALLENGER'] == 'Yes']

r_challengers


r_challengers.describe()


# Calculate the mean for each of the specified columns
means2 = r_challengers[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means2 = means2.sort_values(ascending=False)


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means2)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Title_Loss_%                     69.047172
Win_%                            61.308590
Total_Rounds                     39.657277
Loss_%                           37.258460
Title_Win_%                      28.978448
Title_KO_Loss_%                  28.471005
Title_Dec_Loss_%                 27.892083
Dec_Win_%                        26.484512
KO_Win_%                         24.026060
Dec_Loss_%                       17.045424
Total_Fights                     15.150235
KO_Loss_%                        14.517436
Title_Sub_Loss_%                 12.566713
Title_KO_Win_%                   12.364603
Title_Dec_Win_%                  11.214045
Sub_Win_%                        10.679770
Wins                              9.469484
Sub_Loss_%                        5.639074
Losses                            5.460094
Title_Sub_Win_%                   5.305903
Win_by_Decision                   4.131455
Win_by_KO_TKO                     3.638498
Total_Fights_with_Title           3.089202
Loss_by_Decision                  2.600939
Rounds_per_Fight                  2.583171
Loss_by_KO_TKO                    2.037559
Win_by_Submission                 1.676056
Wins_with_Title                   1.521127
Losses_with_Title                 1.502347
Loss_by_Submission                0.812207
Win_by_KO_TKO_with_Title          0.657277
Loss_by_KO_TKO_with_Title         0.643192
Loss_by_Decision_with_Title       0.591549
Win_by_Decision_with_Title        0.591549
Win_by_Submission_with_Title      0.267606
Loss_by_Submission_with_Title     0.262911
Draws                             0.122066
No_Contests                       0.098592
Draws_with_Title                  0.056338
No_Contests_with_Title            0.009390
dtype: float64


# Champions
r_non_champions = r[r['EITHER_CHAMP'] != 'Yes']

r_non_champions


r_non_champions.describe()


# Calculate the mean for each of the specified columns
means3 = r_non_champions[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means3 = means3.sort_values(ascending=False)


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means3)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Win_%                            68.529475
Title_Win_%                      59.926305
Total_Rounds                     44.213592
Title_Loss_%                     38.417939
Loss_%                           29.899001
Dec_Win_%                        28.718281
KO_Win_%                         28.092761
Title_KO_Win_%                   25.569518
Title_Dec_Win_%                  23.190211
Title_KO_Loss_%                  18.990201
Total_Fights                     16.281553
KO_Loss_%                        13.559472
Title_Dec_Loss_%                 12.453208
Dec_Loss_%                       11.983180
Sub_Win_%                        11.631376
Wins                             11.048544
Title_Sub_Win_%                  10.972401
Title_Sub_Loss_%                  6.731812
Total_Fights_with_Title           5.009709
Losses                            4.970874
Win_by_Decision                   4.747573
Win_by_KO_TKO                     4.388350
Sub_Loss_%                        4.239454
Wins_with_Title                   3.145631
Rounds_per_Fight                  2.694256
Loss_by_Decision                  2.126214
Loss_by_KO_TKO                    2.126214
Win_by_Submission                 1.893204
Losses_with_Title                 1.766990
Win_by_KO_TKO_with_Title          1.359223
Win_by_Decision_with_Title        1.223301
Loss_by_KO_TKO_with_Title         0.844660
Loss_by_Submission                0.699029
Loss_by_Decision_with_Title       0.621359
Win_by_Submission_with_Title      0.553398
Loss_by_Submission_with_Title     0.291262
Draws                             0.135922
No_Contests                       0.126214
Draws_with_Title                  0.077670
No_Contests_with_Title            0.019417
dtype: float64


# Challengers
r_non_challengers = r[r['EVER_CHALLENGER'] != 'Yes']

r_non_challengers


r_non_challengers.describe()


# Calculate the mean for each of the specified columns
means4 = r_non_challengers[metrics].mean()

# Rank the means from highest to lowest and plot again
sorted_means4 = means4.sort_values(ascending=False)


# Display max rows
pd.set_option('display.max_rows', None)

print(sorted_means4)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Title_Loss_%                     69.047172
Win_%                            61.308590
Total_Rounds                     39.657277
Loss_%                           37.258460
Title_Win_%                      28.978448
Title_KO_Loss_%                  28.471005
Title_Dec_Loss_%                 27.892083
Dec_Win_%                        26.484512
KO_Win_%                         24.026060
Dec_Loss_%                       17.045424
Total_Fights                     15.150235
KO_Loss_%                        14.517436
Title_Sub_Loss_%                 12.566713
Title_KO_Win_%                   12.364603
Title_Dec_Win_%                  11.214045
Sub_Win_%                        10.679770
Wins                              9.469484
Sub_Loss_%                        5.639074
Losses                            5.460094
Title_Sub_Win_%                   5.305903
Win_by_Decision                   4.131455
Win_by_KO_TKO                     3.638498
Total_Fights_with_Title           3.089202
Loss_by_Decision                  2.600939
Rounds_per_Fight                  2.583171
Loss_by_KO_TKO                    2.037559
Win_by_Submission                 1.676056
Wins_with_Title                   1.521127
Losses_with_Title                 1.502347
Loss_by_Submission                0.812207
Win_by_KO_TKO_with_Title          0.657277
Loss_by_KO_TKO_with_Title         0.643192
Loss_by_Decision_with_Title       0.591549
Win_by_Decision_with_Title        0.591549
Win_by_Submission_with_Title      0.267606
Loss_by_Submission_with_Title     0.262911
Draws                             0.122066
No_Contests                       0.098592
Draws_with_Title                  0.056338
No_Contests_with_Title            0.009390
dtype: float64


# Display max rows
pd.set_option('display.max_rows', None)

breakdown(event_buyrate)
# Set the display options to show 20 rows at the top and bottom
pd.set_option('display.max_rows', 20)

Distinct Count 
 EVENT      209
DATE       209
Buyrate    108
dtype: int64


 Count 
 EVENT      209
DATE       209
Buyrate    209
dtype: int64


 DTypes 
 EVENT       object
DATE        object
Buyrate    float64
dtype: object


 Shape 
 (209, 3)


 Nulls 
 EVENT      0
DATE       0
Buyrate    0
dtype: int64


 Null % 
 EVENT      0.0
DATE       0.0
Buyrate    0.0
dtype: float64


# Close the cursor and connection
cursor.close()
conn.close()


fb = fighter_buyrate[fighter_buyrate['PPV_Fights'] > 1]

fb.describe()


# Histogram
fb.hist(figsize=(10, 8))

# Box Plot
plt.figure(figsize=(10, 7))
sns.boxplot(data=fb, orient="v")

# Pair Plot
sns.pairplot(fb, hue="STANCE")

<seaborn.axisgrid.PairGrid at 0x7fe0fd305af0>


fb.corr()


correlation_matrix = fb[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()


# Plotting box plots for the numerical columns
plt.figure(figsize=(12, 6))

# Subplot 1: Buyrate
plt.subplot(1, 3, 1)
sns.boxplot(y=fb['Buyrate'])
plt.title('Box Plot of Buyrate')

# Subplot 2: Avg_Buyrate_Per_Event
plt.subplot(1, 3, 2)
sns.boxplot(y=fb['Avg_Buyrate_Per_Event'].dropna())  # Drop NA for visualization
plt.title('Box Plot of Avg Buyrate Per Event')

# Subplot 3: PPV_Fights
plt.subplot(1, 3, 3)
sns.boxplot(y=fb['PPV_Fights'].dropna())  # Drop NA for visualization
plt.title('Box Plot of PPV Fights')

plt.tight_layout()
plt.show()


plt.figure(figsize=(10, 6))
sns.countplot(x='STANCE', data=fb, palette='Set2')
plt.title('Population Distribution by Stance')
plt.xlabel('Stance')
plt.ylabel('Count')
plt.show()


# For demonstration, we'll filter the dataset for 'Orthodox' and 'Southpaw' stances only
# and consider the 'Buyrate' column for A/B testing
orthodox_buyrates = fb[fb['STANCE'] == 'Orthodox']['Buyrate']
southpaw_buyrates = fb[fb['STANCE'] == 'Southpaw']['Buyrate']

# Perform a t-test between the two groups
t_stat, p_value = ttest_ind(orthodox_buyrates, southpaw_buyrates, nan_policy='omit')  # 'omit' to ignore NaNs

t_stat, p_value

(-2.376810039412822, 0.017700976328208187)


champion_buyrates = fb[(fb['EVER_CHAMPION'] == 'Yes') | (fb['EVER_INTERIM'] == 'Yes')]['Buyrate']
non_champion_buyrates = fb[(fb['EVER_CHAMPION'] != 'Yes') | (fb['EVER_INTERIM'] != 'Yes')]['Buyrate']


# Perform a t-test between the two groups
t_stat, p_value = ttest_ind(champion_buyrates, non_champion_buyrates)

t_stat, p_value

(10.324081823558389, 1.0758014507380182e-23)


# Perform ANOVA test
f_stat, p_value = f_oneway(champion_buyrates, non_champion_buyrates)

f_stat, p_value

(106.58666549952886, 1.0758014507374742e-23)


# Perform chi-square test
chi2, p_value, dof, expected = chi2_contingency(fb[['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights']])

print(f"Chi-square statistic: {chi2}, P-value: {p_value}")

Chi-square statistic: 138812015.36374083, P-value: 0.0


# Number of successes (conversions) and samples in each group
successes = np.array([300, 350])
samples = np.array([1000, 1000])

# Perform z-test
z_stat, p_value = proportions_ztest(count=successes, nobs=samples)

print(f"Z-statistic: {z_stat}, P-value: {p_value}")

Z-statistic: -2.3870495801314426, P-value: 0.01698420058213061


# Counting the values in the STANCE column
stance_count = records["STANCE"].value_counts()

# Calculating the percentage of each stance
stance_percentage = records["STANCE"].value_counts(normalize=True) * 100

# Combine count and percentage into a single DataFrame for a better display
stance_summary = pd.DataFrame({'Count': stance_count, 'Percentage': stance_percentage})

stance_summary


# Plotting the counts of the STANCE values
fig, ax = plt.subplots(1, 2, figsize=(14, 6))

# Count plot
stance_count.plot(kind='bar', ax=ax[0], color='sandybrown')
ax[0].set_title('Count of Fighters by Stance')
ax[0].set_xlabel('Stance')
ax[0].set_ylabel('Count')
ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45, ha='right')

# Percentage plot
stance_percentage.plot(kind='pie', ax=ax[1], autopct='%1.1f%%', startangle=140, colors=['skyblue', 'lightgreen', 'salmon', 'gold'])
ax[1].set_ylabel('')
ax[1].set_title('Percentage of Fighters by Stance')

plt.tight_layout()
plt.show()


# Looking at just champions
champion_records = records[(records['EVER_CHAMPION'] == 'Yes') | (records['EVER_INTERIM'] == 'Yes')]

champion_records.sort_values(by='Wins_with_Title', ascending=False, na_position='last')


# Champions make up about 4.5% of the fighter population
105/2317

0.045317220543806644


# Counting the values in the STANCE column
champion_stance_count = champion_records["STANCE"].value_counts()

# Calculating the percentage of each stance
champion_stance_percentage = champion_records["STANCE"].value_counts(normalize=True) * 100

# Combine count and percentage into a single DataFrame for a better display
champion_stance_summary = pd.DataFrame({'Count': champion_stance_count, 'Percentage': champion_stance_percentage})

champion_stance_summary


# Plotting the counts of the STANCE values
fig, ax = plt.subplots(1, 2, figsize=(14, 6))

# Count plot
champion_stance_count.plot(kind='bar', ax=ax[0], color='orange')
ax[0].set_title('Champion Count of Fighters by Stance')
ax[0].set_xlabel('Stance')
ax[0].set_ylabel('Count')
ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45, ha='right')

# Percentage plot
champion_stance_percentage.plot(kind='pie', ax=ax[1], autopct='%1.1f%%', startangle=140, colors=['turquoise', 'seagreen', 'khaki', 'hotpink'])
ax[1].set_ylabel('')
ax[1].set_title('Champion Percentage of Fighters by Stance')

plt.tight_layout()
plt.show()


# Looking at just challengers
challenger_records = records[(records['EVER_CHALLENGER'] == 'Yes')]

challenger_records


# Challengers make up about 9.5% of the fighter population
221/2317

0.0953819594302978


# Counting the values in the STANCE column
challenger_stance_count = challenger_records["STANCE"].value_counts()

# Calculating the percentage of each stance
challenger_stance_percentage = challenger_records["STANCE"].value_counts(normalize=True) * 100

# Combine count and percentage into a single DataFrame for a better display
challenger_stance_summary = pd.DataFrame({'Count': challenger_stance_count, 'Percentage': challenger_stance_percentage})

challenger_stance_summary


top_1 = records[records['FIGHTER'].isin(top_1_percent_list)]

top_1


# Counting the values in the STANCE column
top_1_stance_count = top_1["STANCE"].value_counts()

# Calculating the percentage of each stance
top_1_stance_percentage = top_1["STANCE"].value_counts(normalize=True) * 100

# Combine count and percentage into a single DataFrame for a better display
top_1_stance_summary = pd.DataFrame({'Count': top_1_stance_count, 'Percentage': top_1_stance_percentage})

top_1_stance_summary


top_10 = records[records['FIGHTER'].isin(top_10_percent_list)]

top_10


# Counting the values in the STANCE column
top_10_stance_count = top_10["STANCE"].value_counts()

# Calculating the percentage of each stance
top_10_stance_percentage = top_10["STANCE"].value_counts(normalize=True) * 100

# Combine count and percentage into a single DataFrame for a better display
top_10_stance_summary = pd.DataFrame({'Count': top_10_stance_count, 'Percentage': top_10_stance_percentage})

top_10_stance_summary


# Remove rows with nulls in 'STANCE' column
cleaned_data = records.dropna(subset=['STANCE'])

# Define color map for stances
color_map = {
    'Orthodox': 'red',
    'Southpaw': 'blue',
    'Switch': 'purple'
}


# Adjusting the figure size for a bigger plot
fig, ax = plt.subplots(figsize=(12, 10))  # Increased figure size
for stance, df_group in cleaned_data.groupby('STANCE'):
    for champ, df_champ in df_group.groupby('EITHER_CHAMP'):
        marker = '*' if champ == 'Yes' else 'o'
        color = color_map.get(stance, 'gray')  # Use the defined color map
        ax.scatter(df_champ['Win_%'], df_champ['Total_Fights'], label=f"{stance}, Champ: {champ}", color=color, alpha=0.6, marker=marker)

# Customize the plot
ax.set_xlabel('Win Percentage')
ax.set_ylabel('Total Fights')
ax.set_title('Win Percentage vs. Total Fights by Stance and Championship Status')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
plt.tight_layout()

plt.show()


# Filter the dataframe for Total_Fights greater than 10
df_ten= cleaned_data[cleaned_data['Total_Fights'] > 10]

# Adjusting the figure size for a bigger plot
fig, ax = plt.subplots(figsize=(12, 10))  # Increased figure size
for stance, df_group in df_ten.groupby('STANCE'):
    for champ, df_champ in df_group.groupby('EITHER_CHAMP'):
        marker = '*' if champ == 'Yes' else 'o'
        color = color_map.get(stance, 'gray')  # Use the defined color map
        ax.scatter(df_champ['Win_%'], df_champ['Total_Fights'], label=f"{stance}, Champ: {champ}", color=color, alpha=0.6, marker=marker)

# Customize the plot
ax.set_xlabel('Win Percentage')
ax.set_ylabel('Total Fights')
ax.set_title('Win Percentage vs. Total Fights by Stance and Championship Status')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
plt.tight_layout()

plt.show()


# Prepare the data for clustering
X = records[['Win_%']].dropna()  # Removing rows with NaN in 'Win_%' to avoid errors during clustering

# Elbow method to find the optimal number of clusters
wcss = []  # Within cluster sum of squares
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)

# Plotting the results onto a line graph to observe the 'elbow'
plt.figure(figsize=(10,5))
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')  # Within cluster sum of squares
plt.grid(True)
plt.show()


# Proceed with K-means clustering using an example choice of 3 clusters
kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(X)

# Assigning cluster labels to the data
X['Cluster'] = kmeans.labels_

# Display the first few rows of the dataframe with cluster labels
X.head()


# Prepare the data for clustering including both 'Win_%' and 'Total_Fights'
X_multi = records[['Win_%', 'Total_Fights']].dropna()  # Removing rows with NaN to avoid errors during clustering

# Applying K-means clustering with 3 clusters as a starting example
kmeans_multi = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans_multi.fit(X_multi)

# Assigning cluster labels to the data
X_multi['Cluster'] = kmeans_multi.labels_

# Display the first few rows of the dataframe with cluster labels
X_multi.head()


# Let's visualize the clustering on a scatter plot with 'Win_%' on the x-axis and 'Total_Fights' on the y-axis.
plt.figure(figsize=(12, 8))

# Scatter plot for clusters
for cluster in set(kmeans_multi.labels_):
    filtered_data = X_multi[X_multi['Cluster'] == cluster]
    plt.scatter(filtered_data['Win_%'], filtered_data['Total_Fights'], label=f'Cluster {cluster}')

plt.title('Clustering of Fighters based on Win_% and Total Fights')
plt.xlabel('Win_%')
plt.ylabel('Total Fights')
plt.legend()
plt.grid(True)
plt.show()


# Calculating the average Total_Fights and Win_% by STANCE
average_stats_by_stance = cleaned_data.groupby('STANCE')[['Total_Fights', 'Win_%']].mean()

average_stats_by_stance


# Calculating the average Total_Fights and Win_% by STANCE
average_stats_by_stance = records.groupby('STANCE')[['Total_Fights', 'Win_%']].mean()

# Grouping by STANCE, summing Total Wins and Total Fights
sum_stats_by_stance = records.groupby('STANCE')[['Wins', 'Total_Fights']].sum()

# Creating a new column 'Win_%' by dividing Wins by Total_Fights in the summed data
sum_stats_by_stance['Win_%'] = (sum_stats_by_stance['Wins'] / sum_stats_by_stance['Total_Fights']) * 100

sum_stats_by_stance


# Calculate the correlation between Win_% and Total_Fights
correlation = records['Win_%'].corr(records['Total_Fights'])
correlation

0.4608597384904863


# Calculate the correlation of Win_% to Total_Fights for each value in STANCE
correlation_by_stance = records.groupby('STANCE')['Win_%', 'Total_Fights'].corr().iloc[0::2,-1].reset_index()

# Rename columns for clarity
correlation_by_stance.columns = ['STANCE', 'Measurement', 'Correlation']
correlation_by_stance = correlation_by_stance.drop('Measurement', axis=1)

correlation_by_stance

/var/folders/_6/_1qflrx12_j4kc3ssfs46ldw0000gn/T/ipykernel_1785/3902886768.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  correlation_by_stance = records.groupby('STANCE')['Win_%', 'Total_Fights'].corr().iloc[0::2,-1].reset_index()


# Filter out rows where 'STANCE' is null and then analyze the relationship between 'STANCE' and 'Wins'
filtered_data = records.dropna(subset=['STANCE'])

# Since 'STANCE' is a categorical variable and 'Wins' is numerical, we'll use one-hot encoding for 'STANCE' to perform correlation analysis
stance_dummies = pd.get_dummies(filtered_data['STANCE'])

# Join the encoded stance data with the 'Wins' column
stance_wins_data = stance_dummies.join(filtered_data['Wins'])

# Calculate correlation between stance types and wins
correlation_matrix = stance_wins_data.corr().loc[['Wins']]

correlation_matrix


# For visualization, we'll create a bar plot to show the correlation of each stance with wins.

# Extracting correlation values excluding the last one (Wins with itself)
correlation_values = correlation_matrix.drop(columns=['Wins']).iloc[0]

# Plotting
plt.figure(figsize=(10, 6))
correlation_values.plot(kind='bar', color=['blue', 'green', 'red'])
plt.title('Correlation between Stance and Wins')
plt.xlabel('Stance')
plt.ylabel('Correlation Coefficient')
plt.axhline(0, color='black', linewidth=0.8)  # Line to indicate zero correlation
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--')
plt.show()


# We need to merge the cluster labels back into the original dataset to associate each fighter with their cluster.
# We'll join on the index after ensuring it's aligned between the datasets.
data_with_clusters = records.join(X_multi['Cluster'], how='inner')

# Now we'll filter out rows where 'STANCE' is null to focus on Orthodox, Southpaw, and Switch only.
data_with_clusters_no_nulls = data_with_clusters.dropna(subset=['STANCE'])

# Group by cluster and stance, then count the number of fighters in each category.
stance_distribution_by_cluster = data_with_clusters_no_nulls.groupby(['Cluster', 'STANCE']).size().unstack(fill_value=0)

stance_distribution_by_cluster


# Calculate the percentage of each stance within each cluster
stance_percentage_by_cluster = stance_distribution_by_cluster.div(stance_distribution_by_cluster.sum(axis=1), axis=0) * 100

stance_percentage_by_cluster


# Hsitograms

data = fb

# Setting up the visualization environment
sns.set(style="whitegrid")

# Creating histograms for numerical columns
fig, ax = plt.subplots(3, 1, figsize=(10, 15))

numerical_cols = ['Buyrate', 'Avg_Buyrate_Per_Event', 'PPV_Fights']
for i, col in enumerate(numerical_cols):
    sns.histplot(data[col], bins=30, ax=ax[i], kde=True)
    ax[i].set_title(f'Histogram of {col}', fontsize=15)
    ax[i].set_xlabel('')
    ax[i].set_ylabel('Frequency')

plt.tight_layout()
plt.show()


# Creating box plots for numerical columns
fig, ax = plt.subplots(3, 1, figsize=(10, 15))

for i, col in enumerate(numerical_cols):
    sns.boxplot(x=data[col], ax=ax[i])
    ax[i].set_title(f'Box Plot of {col}', fontsize=15)
    ax[i].set_xlabel('')

plt.tight_layout()
plt.show()


# Cumulative Distribution Function

# Creating CDFs for numerical columns
fig, ax = plt.subplots(3, 1, figsize=(10, 15))

for i, col in enumerate(numerical_cols):
    sns.ecdfplot(data[col], ax=ax[i])
    ax[i].set_title(f'Cumulative Distribution Function of {col}', fontsize=15)
    ax[i].set_xlabel('')
    ax[i].set_ylabel('CDF')

plt.tight_layout()
plt.show()


# Scatterplot

# Creating Q-Q plots for numerical columns
fig, ax = plt.subplots(1, 3, figsize=(18, 6))

for i, col in enumerate(numerical_cols):
    stats.probplot(data[col].dropna(), dist="norm", plot=ax[i])
    ax[i].set_title(f'Q-Q Plot of {col}', fontsize=15)

plt.tight_layout()
plt.show()


# Scatter plot for Buyrate vs. Avg_Buyrate_Per_Event
plt.figure(figsize=(10, 7))
sns.scatterplot(x='Buyrate', y='Avg_Buyrate_Per_Event', data=data)
plt.title('Scatter Plot of Buyrate vs. Avg_Buyrate_Per_Event', fontsize=15)
plt.xlabel('Buyrate')
plt.ylabel('Avg_Buyrate_Per_Event')
plt.show()


# Calculating the correlation matrix
corr_matrix = data[numerical_cols].corr()

# Creating a heatmap to visualize the correlation matrix
plt.figure(figsize=(10, 7))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=.5)
plt.title('Heat Map of Correlation Between Numerical Variables', fontsize=15)
plt.show()


# Creating a scatterplot matrix for the numerical columns
sns.pairplot(data[numerical_cols].dropna())
plt.suptitle('Scatterplot Matrix for Numerical Variables', size=15, y=1.02)
plt.show()


# Creating a violin chart for Buyrate across different STANCE categories
plt.figure(figsize=(10, 7))
sns.violinplot(x='STANCE', y='Buyrate', data=data)
plt.title('Violin Chart of Buyrate by STANCE', fontsize=15)
plt.xlabel('STANCE')
plt.ylabel('Buyrate')
plt.show()


# Visualizing distributions for numerical and categorical variables

# Setting up the visualization environment
fig, ax = plt.subplots(2, 2, figsize=(14, 12))

# Numerical distributions: Histograms for Buyrate and Avg_Buyrate_Per_Event to show potential skewness
sns.histplot(data['Buyrate'], bins=30, kde=True, ax=ax[0, 0])
ax[0, 0].set_title('Histogram of Buyrate', fontsize=12)

sns.histplot(data['Avg_Buyrate_Per_Event'], bins=30, kde=True, ax=ax[0, 1])
ax[0, 1].set_title('Histogram of Avg_Buyrate_Per_Event', fontsize=12)

# Categorical distribution: Bar plot for STANCE
stance_counts = data['STANCE'].value_counts()
stance_counts.plot(kind='bar', ax=ax[1, 0])
ax[1, 0].set_title('Bar Plot of STANCE Categories', fontsize=12)
ax[1, 0].set_ylabel('Frequency')

# Checking for bimodality: Histogram for PPV_Fights
sns.histplot(data['PPV_Fights'], bins=30, kde=True, ax=ax[1, 1])
ax[1, 1].set_title('Histogram of PPV_Fights', fontsize=12)

plt.tight_layout()
plt.show()


# Filtering the dataset for champions and non-champions based on the 'EVER_CHAMPION' column
champions = data[data['EVER_CHAMPION'] == 'Yes']['Buyrate'].dropna()
non_champions = data[data['EVER_CHAMPION'] == 'No']['Buyrate'].dropna()

# Performing a t-test between the two groups
t_stat, p_value = ttest_ind(champions, non_champions, equal_var=False)  # Assuming unequal variances

# Results
t_stat, p_value

(7.543579192229696, 3.8503025640219814e-11)


# Aligning the data by dropping rows where either 'Buyrate' or 'PPV_Fights' is NaN
aligned_data = data.dropna(subset=['Buyrate', 'PPV_Fights'])

# Recalculating the correlation coefficient
correlation_coefficient, p_value_corr = stats.pearsonr(aligned_data['Buyrate'], aligned_data['PPV_Fights'])

correlation_coefficient, p_value_corr

(0.8679961005667428, 8.06575271497532e-252)


# Filtering the data for different stances and their buyrates
stances = data['STANCE'].dropna().unique()
stance_groups = [data[data['STANCE'] == stance]['Buyrate'].dropna() for stance in stances]

# Performing ANOVA
anova_result = f_oneway(*stance_groups)

anova_result.statistic, anova_result.pvalue

(3.075624901955279, 0.04669368569161873)


# Creating a contingency table for EVER_CHAMPION and EVER_INTERIM
contingency_table = pd.crosstab(data['EVER_CHAMPION'], data['EVER_INTERIM'])

# Performing Chi-Square Test for Independence
chi2, p_value_chi, dof, expected = chi2_contingency(contingency_table)

chi2, p_value_chi

(44.36607327242141, 2.7236655263074702e-11)


# Hypothetical population mean
hypothetical_mean = 500000

# Performing a one-sample t-test
t_stat_one_sample, p_value_one_sample = ttest_1samp(data['Buyrate'].dropna(), hypothetical_mean)

t_stat_one_sample, p_value_one_sample

(26.096225616788583, 7.875443393880771e-110)


# Calculate the Test Statistic
# We already have the groups defined: champions vs. non-champions

# Determine the P-Value
# Reusing the two-sample t-test calculation from earlier for the same groups
t_stat_ab, p_value_ab = ttest_ind(champions, non_champions, equal_var=False)

# Displaying the test statistic and p-value as part of the A/B testing steps
t_stat_ab, p_value_ab

(7.543579192229696, 3.8503025640219814e-11)


# First, calculate the total cumulative buyrate to understand the target for 80%
df = fb

total_buyrate = df['Buyrate'].sum()

# Calculate 80% of the total cumulative buyrate
target_buyrate = total_buyrate * 0.8

# Sort the dataframe by 'Buyrate' descending
df_sorted_by_buyrate = df.sort_values(by='Buyrate', ascending=False)

# Calculate the cumulative sum of buyrates
df_sorted_by_buyrate['cumulative_sum'] = df_sorted_by_buyrate['Buyrate'].cumsum()

# Find the smallest number of top buyrates that make up at least 80% of the total
top_buyrates_for_80_percent = df_sorted_by_buyrate[df_sorted_by_buyrate['cumulative_sum'] <= target_buyrate]

top_buyrates_for_80_percent

	EVENT	DATE	Buyrate
76	UFC 274: Oliveira vs. Gaethje	2022-05-07	400000.0
92	UFC 269: Oliveira vs. Poirier	2021-12-11	500000.0
96	UFC 268: Usman vs. Covington 2	2021-11-06	700000.0
111	UFC 264: Poirier vs. McGregor 3	2021-07-10	1800000.0
114	UFC 263: Adesanya vs. Vettori 2	2021-06-12	600000.0
...	...	...	...
639	UFC 37: High Impact	2002-05-10	50000.0
640	UFC 36: Worlds Collide	2002-03-22	55000.0
641	UFC 35: Throwdown	2002-01-11	35000.0
642	UFC 34: High Voltage	2001-11-02	65000.0
643	UFC 33: Victory in Vegas	2001-09-28	75000.0

	FIGHTER	STANCE	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	EITHER_CHAMP	HEIGHT	WEIGHT	REACH	APE_INDEX	FRAME	HEIGHT_DIFF	REACH_DIFF	AGE_DIFF	AI_DIFF	FRAME_DIFF	OPP_HEIGHT	OPP_WEIGHT	OPP_REACH	OPP_APE_INDEX	OPP_FRAME	OPP_REACH_DIFF	OPP_AGE_DIFF	OPP_AI_DIFF	OPP_FRAME_DIFF	HEIGHT_DELTA	REACH_DELTA	AGE_DELTA	AI_DELTA	FRAME_DELTA	HEIGHT_DELTA_RANK	REACH_DELTA_RANK	AGE_DELTA_RANK	AI_DELTA_RANK	FRAME_DELTA_RANK	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
0	Asu Almabayev	None	No	No	No	No	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	66.000000	135.000000	68.000000	1.030303	67.000000	1.124836	3.432091	0.010375	0.773653	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	CJ Vergara	Orthodox	No	No	No	No	66.0	135.0	68.0	1.030303	67.0	0.422470	1.124836	2.432091	0.010375	0.773653	66.500000	125.000000	70.000000	1.052352	68.250000	3.124836	-2.817909	0.032424	2.023653	-0.500000	-2.000000	4.750000	-0.022049	-1.250000	277.0	398.0	91.0	1404.0	444.0	42.521859	27.105384	30.556834
2	Curtis Blaydes	Orthodox	No	No	No	No	76.0	265.0	80.0	1.052632	78.0	1.138963	2.904203	-3.661011	0.022708	2.021583	74.888889	245.777778	78.222222	1.044572	76.555556	1.126425	1.561212	0.014648	0.577138	1.111111	1.777778	-5.222222	0.008060	1.444444	143.0	146.0	538.0	666.0	172.0	74.436263	79.682467	78.485964
3	Jailton Almeida	Orthodox	No	No	No	No	75.0	205.0	79.0	1.053333	77.0	1.066832	2.927534	-0.532982	0.024388	1.997183	75.142857	239.571429	77.285714	1.028885	76.214286	1.213248	3.609875	-0.000060	1.211469	-0.142857	1.714286	-4.142857	0.024448	0.785714	246.0	151.0	500.0	346.0	225.0	48.780488	79.061206	69.397147
4	Benoit Saint Denis	Southpaw	No	No	No	No	71.0	170.0	73.0	1.028169	72.0	1.203161	1.438362	-2.626320	0.002739	1.320761	69.857143	157.857143	71.428571	1.022399	70.642857	-0.133067	1.945109	-0.003031	-0.036382	1.142857	1.571429	-4.571429	0.005770	1.357143	142.0	159.0	517.0	728.0	177.0	74.574321	77.542568	77.841694
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2312	Joao Roque	Orthodox	No	No	No	No	66.0	155.0	66.0	1.000000	66.0	-3.541575	-5.279980	-1.101245	-0.025164	-4.410778	67.000000	145.000000	70.000000	1.044776	68.500000	-1.279980	-4.101245	0.019612	-1.910778	-1.000000	-4.000000	3.000000	-0.044776	-2.500000	319.0	479.0	150.0	1792.0	532.0	32.512655	9.433962	14.450069
2313	Marcelo Aguiar	Orthodox	No	No	No	No	70.0	170.0	70.0	1.000000	70.0	-1.328423	-3.251590	NaN	-0.027023	-2.290007	69.000000	170.000000	73.000000	1.057971	71.000000	-0.251590	-3.776772	0.030948	-1.290007	1.000000	-3.000000	NaN	-0.057971	-1.000000	148.0	448.0	NaN	1868.0	416.0	71.514036	16.175794	35.687989
2314	Adrian Serrano	None	No	No	No	No	68.0	170.0	68.0	1.000000	68.0	-3.328423	-5.251590	6.223228	-0.027023	-4.290007	70.000000	170.000000	70.000000	1.000000	70.000000	-3.251590	-1.776772	-0.027023	-2.290007	-2.000000	-2.000000	8.000000	0.000000	-2.000000	379.0	398.0	29.0	839.0	506.0	16.820064	27.105384	19.696272
2315	David Dodd	Orthodox	No	No	No	No	74.0	200.0	74.0	1.000000	74.0	1.331878	-0.782579	-4.365562	-0.029232	0.274650	69.000000	185.000000	69.000000	1.000000	69.000000	-5.782579	NaN	-0.029232	-4.725350	5.000000	5.000000	NaN	0.000000	5.000000	12.0	30.0	NaN	839.0	17.0	98.918546	97.468937	98.803497
2316	Tyrone Roberts	Orthodox	No	No	No	No	69.0	185.0	69.0	1.000000	69.0	-3.668122	-5.782579	NaN	-0.029232	-4.725350	74.000000	200.000000	74.000000	1.000000	74.000000	-0.782579	-4.365562	-0.029232	0.274650	-5.000000	-5.000000	NaN	0.000000	-5.000000	458.0	497.0	NaN	839.0	604.0	1.265532	4.970087	1.978831

	FIGHTER	STANCE	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	EITHER_CHAMP	FA_FIGHTER	FA_KD	FA_TD	FA_SUB.ATT	FA_REV	FA_CTRL	FA_HEAD	FA_BODY	FA_LEG	FA_DISTANCE	FA_CLINCH	FA_GROUND	FA_SIG	FA_TOT.SIG	FA_STR	FA_TOTAL.STR	FA_TOTAL.TD	FA_TOTAL.HEAD	FA_TOTAL.BODY	FA_TOTAL.LEG	FA_TOTAL.DISTANCE	FA_TOTAL.CLINCH	FA_TOTAL.GROUND	FA_OPP_KD	FA_OPP_TD	FA_OPP_SUB.ATT	FA_OPP_REV	FA_OPP_CTRL	FA_OPP_HEAD	FA_OPP_BODY	FA_OPP_LEG	FA_OPP_DISTANCE	FA_OPP_CLINCH	FA_OPP_GROUND	FA_OPP_SIG	FA_OPP_TOT.SIG	FA_OPP_STR	FA_OPP_TOTAL.STR	FA_OPP_TOTAL.TD	FA_OPP_TOTAL.HEAD	FA_OPP_TOTAL.BODY	FA_OPP_TOTAL.LEG	FA_OPP_TOTAL.DISTANCE	FA_OPP_TOTAL.CLINCH	FA_OPP_TOTAL.GROUND	FA_TD_DEF	FA_HEAD_DEF	FA_BODY_DEF	FA_LEG_DEF	FA_DISTANCE_DEF	FA_CLINCH_DEF	FA_GROUND_DEF	FA_SIG_DEF	FA_STR_DEF	FA_TD_%	FA_HEAD_%	FA_BODY_%	FA_LEG_%	FA_DISTANCE_%	FA_CLINCH_%	FA_GROUND_%	FA_SIG_%	FA_STR_%	FA_TD_DEF_%	FA_HEAD_DEF_%	FA_BODY_DEF_%	FA_LEG_DEF_%	FA_DISTANCE_DEF_%	FA_CLINCH_DEF_%	FA_GROUND_DEF_%	FA_SIG_DEF_%	FA_STR_DEF_%
0	Asu Almabayev	None	No	No	No	No	Asu Almabayev	0.000000	3.000000	0.000000	0.000000	3.177778	7.333333	3.000000	4.333333	7.666667	2.666667	4.333333	14.666667	25.666667	28.333333	44.000000	4.666667	17.666667	3.666667	4.333333	17.000000	3.333333	5.333333	0.000000	0.000000	0.000000	0.000000	0.077778	4.333333	4.666667	0.666667	7.333333	1.333333	1.000000	9.666667	17.666667	22.000000	30.666667	0.000000	10.666667	5.666667	1.333333	15.333333	1.333333	1.000000	0.000000	6.333333	1.000000	0.666667	8.000000	0.000000	0.000000	8.000000	8.666667	0.642857	0.415094	0.818182	1.000000	0.450980	0.800000	0.812500	0.571429	0.643939	NaN	0.593750	0.176471	0.500000	0.521739	0.000000	0.000000	0.452830	0.282609
1	CJ Vergara	Orthodox	No	No	No	No	CJ Vergara	0.000000	0.000000	0.083333	0.083333	0.694444	13.222222	8.861111	3.194444	17.111111	2.027778	6.138889	25.277778	43.722222	35.694444	55.388889	0.000000	28.388889	11.694444	3.638889	31.944444	2.694444	9.083333	0.166667	1.000000	0.388889	0.111111	1.316204	13.611111	5.500000	2.944444	19.027778	1.527778	1.500000	22.055556	46.583333	26.611111	52.277778	2.500000	36.055556	7.333333	3.194444	42.500000	2.083333	2.000000	1.500000	22.444444	1.833333	0.250000	23.472222	0.555556	0.500000	24.527778	25.666667	NaN	0.465753	0.757720	0.877863	0.535652	0.752577	0.675841	0.578145	0.644433	0.600000	0.622496	0.250000	0.078261	0.552288	0.266667	0.250000	0.526535	0.490967
2	Curtis Blaydes	Orthodox	No	No	No	No	Curtis Blaydes	0.055556	1.266667	0.000000	0.000000	1.519198	9.433333	1.475926	2.385185	6.462963	1.612963	5.218519	13.294444	27.970370	22.492593	39.153704	2.444444	23.518519	1.692593	2.759259	19.392593	2.029630	6.548148	0.157407	0.344444	0.037037	0.000000	0.201883	6.401852	1.224074	0.727778	6.603704	1.101852	0.648148	8.353704	19.874074	12.364815	24.161111	0.485185	17.364815	1.670370	0.838889	17.751852	1.344444	0.777778	0.140741	10.962963	0.446296	0.111111	11.148148	0.242593	0.129630	11.520370	11.796296	0.518182	0.401102	0.871991	0.864430	0.333270	0.794708	0.796946	0.475305	0.574469	0.290076	0.631332	0.267184	0.132450	0.627999	0.180441	0.166667	0.579668	0.488235
3	Jailton Almeida	Orthodox	No	No	No	No	Jailton Almeida	0.000000	2.028571	0.685714	0.028571	3.565476	13.557143	0.571429	0.100000	0.428571	0.085714	13.714286	14.228571	22.457143	26.142857	37.014286	3.000000	21.228571	0.857143	0.371429	1.771429	0.228571	20.457143	0.000000	0.000000	0.000000	0.000000	0.037857	1.700000	0.085714	0.142857	0.271429	0.028571	1.628571	1.928571	3.400000	5.800000	8.042857	0.057143	3.085714	0.171429	0.142857	1.142857	0.028571	2.228571	0.057143	1.385714	0.085714	0.000000	0.871429	0.000000	0.600000	1.471429	2.242857	0.676190	0.638627	0.666667	0.269231	0.241935	0.375000	0.670391	0.633588	0.706291	1.000000	0.449074	0.500000	0.000000	0.762500	0.000000	0.269231	0.432773	0.278863
4	Benoit Saint Denis	Southpaw	No	No	No	No	Benoit Saint Denis	0.357143	1.238095	0.500000	0.142857	1.931746	10.833333	6.380952	3.047619	11.404762	3.642857	5.214286	20.261905	37.119048	32.666667	51.357143	3.238095	25.642857	8.285714	3.190476	23.309524	4.690476	9.119048	0.071429	0.428571	0.428571	0.000000	0.255952	9.857143	4.261905	1.619048	11.976190	3.238095	0.523810	15.738095	27.547619	18.952381	31.571429	0.976190	21.023810	4.785714	1.738095	22.642857	4.380952	0.523810	0.547619	11.166667	0.523810	0.119048	10.666667	1.142857	0.000000	11.809524	12.619048	0.382353	0.422470	0.770115	0.955224	0.489275	0.776650	0.571802	0.545863	0.636069	0.560976	0.531144	0.109453	0.068493	0.471083	0.260870	0.000000	0.428695	0.399698
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2312	Joao Roque	Orthodox	No	No	No	No	Joao Roque	0.000000	0.333333	0.000000	0.000000	0.088889	0.666667	0.666667	0.333333	1.666667	0.000000	0.000000	1.666667	19.666667	1.666667	19.666667	6.000000	14.333333	2.000000	3.333333	19.000000	0.333333	0.333333	0.000000	0.000000	0.000000	0.333333	0.738889	1.333333	0.000000	0.000000	1.000000	0.333333	0.000000	1.333333	28.000000	6.000000	32.666667	0.000000	28.000000	0.000000	0.000000	25.666667	0.333333	2.000000	0.000000	26.666667	0.000000	0.000000	24.666667	0.000000	2.000000	26.666667	26.666667	0.055556	0.046512	0.333333	0.100000	0.087719	0.000000	0.000000	0.084746	0.084746	NaN	0.952381	NaN	NaN	0.961039	0.000000	1.000000	0.952381	0.816327
2313	Marcelo Aguiar	Orthodox	No	No	No	No	Marcelo Aguiar	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	2.000000	2.000000	2.000000	21.000000	21.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	2.000000	0.000000	0.000000	4.133333	14.000000	4.000000	0.000000	0.000000	0.000000	18.000000	18.000000	31.000000	29.000000	42.000000	3.000000	27.000000	4.000000	0.000000	2.000000	0.000000	29.000000	1.000000	13.000000	0.000000	0.000000	2.000000	0.000000	11.000000	13.000000	13.000000	NaN	1.000000	NaN	NaN	NaN	NaN	1.000000	1.000000	1.000000	0.333333	0.481481	0.000000	NaN	1.000000	NaN	0.379310	0.419355	0.309524
2314	Adrian Serrano	None	No	No	No	No	Adrian Serrano	0.000000	0.000000	0.000000	0.500000	1.558333	1.000000	2.000000	0.000000	1.500000	1.500000	0.000000	3.000000	11.500000	27.500000	39.500000	1.000000	8.000000	3.500000	0.000000	7.500000	4.000000	0.000000	0.000000	1.500000	0.000000	0.000000	1.000000	7.500000	2.500000	3.000000	10.500000	1.500000	1.000000	13.000000	27.500000	25.500000	42.500000	3.000000	20.500000	3.500000	3.500000	22.000000	2.500000	3.000000	1.500000	13.000000	1.000000	0.500000	11.500000	1.000000	2.000000	14.500000	17.000000	0.000000	0.125000	0.571429	NaN	0.200000	0.375000	NaN	0.260870	0.696203	0.500000	0.634146	0.285714	0.142857	0.522727	0.400000	0.666667	0.527273	0.400000
2315	David Dodd	Orthodox	No	No	No	No	David Dodd	0.000000	0.000000	0.000000	0.000000	0.011111	1.666667	1.666667	0.000000	1.000000	1.666667	0.666667	3.333333	10.333333	9.000000	16.333333	0.666667	8.333333	2.000000	0.000000	7.333333	2.000000	1.000000	0.000000	0.333333	0.000000	0.000000	1.594444	3.666667	1.000000	0.666667	4.000000	1.333333	0.000000	5.333333	20.000000	35.000000	54.333333	0.333333	17.666667	1.666667	0.666667	15.000000	4.333333	0.666667	0.000000	14.000000	0.666667	0.000000	11.000000	3.000000	0.666667	14.666667	19.333333	0.000000	0.200000	0.833333	NaN	0.136364	0.833333	0.666667	0.322581	0.551020	0.000000	0.792453	0.400000	0.000000	0.733333	0.692308	1.000000	0.733333	0.355828
2316	Tyrone Roberts	Orthodox	No	No	No	No	Tyrone Roberts	0.000000	0.333333	0.000000	0.000000	1.594444	3.666667	1.000000	0.666667	4.000000	1.333333	0.000000	5.333333	20.000000	35.000000	54.333333	0.333333	17.666667	1.666667	0.666667	15.000000	4.333333	0.666667	0.000000	0.000000	0.000000	0.000000	0.011111	1.666667	1.666667	0.000000	1.000000	1.666667	0.666667	3.333333	10.333333	9.000000	16.333333	0.666667	8.333333	2.000000	0.000000	7.333333	2.000000	1.000000	0.666667	6.666667	0.333333	0.000000	6.333333	0.333333	0.333333	7.000000	7.333333	1.000000	0.207547	0.600000	1.000000	0.266667	0.307692	0.000000	0.266667	0.644172	1.000000	0.800000	0.166667	NaN	0.863636	0.166667	0.333333	0.677419	0.448980

	FIGHTER	STANCE	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	EITHER_CHAMP	RA_FIGHTER	RA_KD	RA_TD	RA_SUB.ATT	RA_REV	RA_CTRL	RA_HEAD	RA_BODY	RA_LEG	RA_DISTANCE	RA_CLINCH	RA_GROUND	RA_SIG	RA_TOT.SIG	RA_STR	RA_TOTAL.STR	RA_TOTAL.TD	RA_TOTAL.HEAD	RA_TOTAL.BODY	RA_TOTAL.LEG	RA_TOTAL.DISTANCE	RA_TOTAL.CLINCH	RA_TOTAL.GROUND	RA_OPP_KD	RA_OPP_TD	RA_OPP_SUB.ATT	RA_OPP_REV	RA_OPP_CTRL	RA_OPP_HEAD	RA_OPP_BODY	RA_OPP_LEG	RA_OPP_DISTANCE	RA_OPP_CLINCH	RA_OPP_GROUND	RA_OPP_SIG	RA_OPP_TOT.SIG	RA_OPP_STR	RA_OPP_TOTAL.STR	RA_OPP_TOTAL.TD	RA_OPP_TOTAL.HEAD	RA_OPP_TOTAL.BODY	RA_OPP_TOTAL.LEG	RA_OPP_TOTAL.DISTANCE	RA_OPP_TOTAL.CLINCH	RA_OPP_TOTAL.GROUND	RA_TD_DEF	RA_HEAD_DEF	RA_BODY_DEF	RA_LEG_DEF	RA_DISTANCE_DEF	RA_CLINCH_DEF	RA_GROUND_DEF	RA_SIG_DEF	RA_STR_DEF	RA_TD_%	RA_HEAD_%	RA_BODY_%	RA_LEG_%	RA_DISTANCE_%	RA_CLINCH_%	RA_GROUND_%	RA_SIG_%	RA_STR_%	RA_TD_DEF_%	RA_HEAD_DEF_%	RA_BODY_DEF_%	RA_LEG_DEF_%	RA_DISTANCE_DEF_%	RA_CLINCH_DEF_%	RA_GROUND_DEF_%	RA_SIG_DEF_%	RA_STR_DEF_%
0	Asu Almabayev	None	No	No	No	No	Asu Almabayev	0.000000	3.000000	0.000000	0.000000	3.177778	7.333333	3.000000	4.333333	7.666667	2.666667	4.333333	14.666667	25.666667	28.333333	44.000000	4.666667	17.666667	3.666667	4.333333	17.000000	3.333333	5.333333	0.000000	0.000000	0.000000	0.000000	0.077778	4.333333	4.666667	0.666667	7.333333	1.333333	1.000000	9.666667	17.666667	22.000000	30.666667	0.000000	10.666667	5.666667	1.333333	15.333333	1.333333	1.000000	0.000000	6.333333	1.000000	0.666667	8.000000	0.000000	0.000000	8.000000	8.666667	0.642857	0.415094	0.818182	1.000000	0.450980	0.800000	0.812500	0.571429	0.643939	NaN	0.593750	0.176471	0.500000	0.521739	0.000000	0.000000	0.452830	0.282609
1	CJ Vergara	Orthodox	No	No	No	No	CJ Vergara	0.000000	0.000000	0.055556	0.055556	0.681481	12.750000	9.861111	3.083333	18.250000	2.500000	4.944444	25.694444	44.611111	37.111111	57.305556	0.000000	28.472222	12.638889	3.500000	34.111111	3.250000	7.250000	0.111111	1.000000	0.305556	0.138889	1.258796	14.000000	5.777778	2.916667	19.722222	1.527778	1.444444	22.694444	48.111111	27.361111	54.027778	2.555556	37.333333	7.638889	3.138889	44.250000	2.027778	1.833333	1.555556	23.333333	1.861111	0.222222	24.527778	0.500000	0.388889	25.416667	26.666667	NaN	0.447805	0.780220	0.880952	0.535016	0.769231	0.681992	0.575965	0.647601	0.608696	0.625000	0.243636	0.070796	0.554300	0.246575	0.212121	0.528291	0.493573
2	Curtis Blaydes	Orthodox	No	No	No	No	Curtis Blaydes	0.024444	1.886667	0.000000	0.000000	2.670889	8.462222	1.548889	1.500000	5.433333	1.900000	4.177778	11.511111	21.655556	22.500000	35.106667	3.837778	18.253333	1.726667	1.675556	14.168889	2.197778	5.288889	0.046667	0.333333	0.026667	0.000000	0.216815	5.788889	1.411111	0.400000	6.882222	0.542222	0.175556	7.600000	19.677778	26.988889	40.008889	0.613333	17.313333	1.906667	0.457778	18.555556	0.902222	0.220000	0.280000	11.524444	0.495556	0.057778	11.673333	0.360000	0.044444	12.077778	13.020000	0.491604	0.463599	0.897040	0.895225	0.383469	0.864510	0.789916	0.531555	0.640904	0.456522	0.665640	0.259907	0.126214	0.629102	0.399015	0.202020	0.613778	0.325428
3	Jailton Almeida	Orthodox	No	No	No	No	Jailton Almeida	0.000000	1.476190	0.304762	0.200000	3.737937	8.580952	0.571429	0.266667	0.647619	0.600000	8.171429	9.419048	14.228571	27.542857	35.980952	3.409524	12.971429	0.628571	0.628571	1.409524	1.600000	11.219048	0.000000	0.000000	0.000000	0.000000	0.198730	3.676190	0.257143	0.028571	0.323810	0.200000	3.438095	3.961905	7.352381	5.923810	10.219048	0.400000	6.809524	0.514286	0.028571	1.933333	0.200000	5.219048	0.400000	3.133333	0.257143	0.000000	1.609524	0.000000	1.780952	3.390476	4.295238	0.432961	0.661527	0.909091	0.424242	0.459459	0.375000	0.728353	0.661981	0.765484	1.000000	0.460140	0.500000	0.000000	0.832512	0.000000	0.341241	0.461140	0.420317
4	Benoit Saint Denis	Southpaw	No	No	No	No	Benoit Saint Denis	0.209524	0.895238	0.257143	0.047619	1.433492	11.952381	6.561905	2.961905	14.514286	2.761905	4.200000	21.476190	39.304762	29.761905	49.047619	3.780952	28.190476	8.057143	3.057143	28.009524	3.504762	7.790476	0.066667	0.209524	0.323810	0.000000	0.547302	11.952381	6.828571	2.657143	16.561905	3.580952	1.295238	21.438095	38.009524	25.419048	42.790476	0.676190	27.085714	7.885714	3.038095	31.771429	4.942857	1.295238	0.466667	15.133333	1.057143	0.380952	15.209524	1.361905	0.000000	16.571429	17.371429	0.236776	0.423986	0.814421	0.968847	0.518191	0.788043	0.539120	0.546402	0.606796	0.690141	0.558720	0.134058	0.125392	0.478717	0.275530	0.000000	0.435981	0.405965
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2312	Joao Roque	Orthodox	No	No	No	No	Joao Roque	0.000000	0.333333	0.000000	0.000000	0.088889	0.666667	0.666667	0.333333	1.666667	0.000000	0.000000	1.666667	19.666667	1.666667	19.666667	6.000000	14.333333	2.000000	3.333333	19.000000	0.333333	0.333333	0.000000	0.000000	0.000000	0.333333	0.738889	1.333333	0.000000	0.000000	1.000000	0.333333	0.000000	1.333333	28.000000	6.000000	32.666667	0.000000	28.000000	0.000000	0.000000	25.666667	0.333333	2.000000	0.000000	26.666667	0.000000	0.000000	24.666667	0.000000	2.000000	26.666667	26.666667	0.055556	0.046512	0.333333	0.100000	0.087719	0.000000	0.000000	0.084746	0.084746	NaN	0.952381	NaN	NaN	0.961039	0.000000	1.000000	0.952381	0.816327
2313	Marcelo Aguiar	Orthodox	No	No	No	No	Marcelo Aguiar	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	2.000000	2.000000	2.000000	21.000000	21.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	2.000000	0.000000	2.000000	0.000000	0.000000	4.133333	14.000000	4.000000	0.000000	0.000000	0.000000	18.000000	18.000000	31.000000	29.000000	42.000000	3.000000	27.000000	4.000000	0.000000	2.000000	0.000000	29.000000	1.000000	13.000000	0.000000	0.000000	2.000000	0.000000	11.000000	13.000000	13.000000	NaN	1.000000	NaN	NaN	NaN	NaN	1.000000	1.000000	1.000000	0.333333	0.481481	0.000000	NaN	1.000000	NaN	0.379310	0.419355	0.309524
2314	Adrian Serrano	None	No	No	No	No	Adrian Serrano	0.000000	0.000000	0.000000	0.500000	1.558333	1.000000	2.000000	0.000000	1.500000	1.500000	0.000000	3.000000	11.500000	27.500000	39.500000	1.000000	8.000000	3.500000	0.000000	7.500000	4.000000	0.000000	0.000000	1.500000	0.000000	0.000000	1.000000	7.500000	2.500000	3.000000	10.500000	1.500000	1.000000	13.000000	27.500000	25.500000	42.500000	3.000000	20.500000	3.500000	3.500000	22.000000	2.500000	3.000000	1.500000	13.000000	1.000000	0.500000	11.500000	1.000000	2.000000	14.500000	17.000000	0.000000	0.125000	0.571429	NaN	0.200000	0.375000	NaN	0.260870	0.696203	0.500000	0.634146	0.285714	0.142857	0.522727	0.400000	0.666667	0.527273	0.400000
2315	David Dodd	Orthodox	No	No	No	No	David Dodd	0.000000	0.000000	0.000000	0.000000	0.011111	1.666667	1.666667	0.000000	1.000000	1.666667	0.666667	3.333333	10.333333	9.000000	16.333333	0.666667	8.333333	2.000000	0.000000	7.333333	2.000000	1.000000	0.000000	0.333333	0.000000	0.000000	1.594444	3.666667	1.000000	0.666667	4.000000	1.333333	0.000000	5.333333	20.000000	35.000000	54.333333	0.333333	17.666667	1.666667	0.666667	15.000000	4.333333	0.666667	0.000000	14.000000	0.666667	0.000000	11.000000	3.000000	0.666667	14.666667	19.333333	0.000000	0.200000	0.833333	NaN	0.136364	0.833333	0.666667	0.322581	0.551020	0.000000	0.792453	0.400000	0.000000	0.733333	0.692308	1.000000	0.733333	0.355828
2316	Tyrone Roberts	Orthodox	No	No	No	No	Tyrone Roberts	0.000000	0.333333	0.000000	0.000000	1.594444	3.666667	1.000000	0.666667	4.000000	1.333333	0.000000	5.333333	20.000000	35.000000	54.333333	0.333333	17.666667	1.666667	0.666667	15.000000	4.333333	0.666667	0.000000	0.000000	0.000000	0.000000	0.011111	1.666667	1.666667	0.000000	1.000000	1.666667	0.666667	3.333333	10.333333	9.000000	16.333333	0.666667	8.333333	2.000000	0.000000	7.333333	2.000000	1.000000	0.666667	6.666667	0.333333	0.000000	6.333333	0.333333	0.333333	7.000000	7.333333	1.000000	0.207547	0.600000	1.000000	0.266667	0.307692	0.000000	0.266667	0.644172	1.000000	0.800000	0.166667	NaN	0.863636	0.166667	0.333333	0.677419	0.448980

	APE_INDEX	HEIGHT_DELTA	REACH_DELTA	AGE_DELTA	FRAME_DELTA
count	6986.000000	6901.000000	6901.000000	6954.000000	6901.000000
mean	1.026002	0.114621	0.373859	-0.793069	0.244240
std	0.027321	2.540008	3.301459	5.194162	2.654312
min	0.920635	-13.000000	-12.000000	-18.000000	-12.500000
25%	1.000000	-1.000000	-2.000000	-4.000000	-1.500000
50%	1.027397	0.000000	0.000000	-1.000000	0.000000
75%	1.043478	2.000000	2.000000	3.000000	2.000000
max	1.120000	12.000000	13.000000	16.000000	11.500000

	EVENT	BOUT	ROUND	FIGHTER	KD	TD	SUB.ATT	REV	CTRL	HEAD	BODY	LEG	DISTANCE	CLINCH	GROUND	SIG	TOT.SIG	SIG_%	STR	TOTAL.STR	TOT_%	TOTAL.TD	TD_%	TOTAL.HEAD	HEAD_%	TOTAL.BODY	BODY_%	TOTAL.LEG	LEG_%	TOTAL.DISTANCE	DISTANCE_%	TOTAL.CLINCH	CLINCH_%	TOTAL.GROUND	GROUND_%	HEIGHT	WEIGHT	REACH	STANCE	DOB	APE_INDEX	FRAME	FIRST	LAST	NICKNAME	OUTCOME	WEIGHTCLASS	METHOD	FINISH_ROUND	TIME	TIME_FORMAT	REFEREE	DETAILS	TITLE	GENDER	DATE	LOCATION	CITY	STATE	COUNTRY	Gate	Buyrate	RESULT	AGE	OPP	HEIGHT_AVG	REACH_AVG	AGE_AVG	AI_AVG	FRAME_AVG	HEIGHT_DIFF	REACH_DIFF	AGE_DIFF	AI_DIFF	FRAME_DIFF	KEY	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	OPP_EVENT	OPP_BOUT	OPP_ROUND	OPP_FIGHTER	OPP_KD	OPP_TD	OPP_SUB.ATT	OPP_REV	OPP_CTRL	OPP_HEAD	OPP_BODY	OPP_LEG	OPP_DISTANCE	OPP_CLINCH	OPP_GROUND	OPP_SIG	OPP_TOT.SIG	OPP_SIG_%	OPP_STR	OPP_TOTAL.STR	OPP_TOT_%	OPP_TOTAL.TD	OPP_TD_%	OPP_TOTAL.HEAD	OPP_HEAD_%	OPP_TOTAL.BODY	OPP_BODY_%	OPP_TOTAL.LEG	OPP_LEG_%	OPP_TOTAL.DISTANCE	OPP_DISTANCE_%	OPP_TOTAL.CLINCH	OPP_CLINCH_%	OPP_TOTAL.GROUND	OPP_GROUND_%	OPP_HEIGHT	OPP_WEIGHT	OPP_REACH	OPP_STANCE	OPP_DOB	OPP_APE_INDEX	OPP_FRAME	OPP_FIRST	OPP_LAST	OPP_NICKNAME	OPP_OUTCOME	OPP_WEIGHTCLASS	OPP_METHOD	OPP_FINISH_ROUND	OPP_TIME	OPP_TIME_FORMAT	OPP_REFEREE	OPP_DETAILS	OPP_TITLE	OPP_GENDER	OPP_DATE	OPP_LOCATION	OPP_CITY	OPP_STATE	OPP_COUNTRY	OPP_Gate	OPP_Buyrate	OPP_RESULT	OPP_AGE	OPP_OPP	OPP_HEIGHT_AVG	OPP_REACH_AVG	OPP_AGE_AVG	OPP_AI_AVG	OPP_FRAME_AVG	OPP_HEIGHT_DIFF	OPP_REACH_DIFF	OPP_AGE_DIFF	OPP_AI_DIFF	OPP_FRAME_DIFF	OPP_KEY	OPP_EVER_CHAMPION	OPP_EVER_INTERIM	OPP_EVER_CHALLENGER	HEIGHT_DELTA	REACH_DELTA	AGE_DELTA	AI_DELTA	FRAME_DELTA	TD_DEF	TD_DEF%	HEAD_DEF	HEAD_DEF%	BODY_DEF	BODY_DEF%	LEG_DEF	LEG_DEF%	DISTANCE_DEF	DISTANCE_DEF%	CLINCH_DEF	CLINCH_DEF%	GROUND_DEF	GROUND_DEF%	SIG_DEF	SIG_DEF%	STR_DEF	STR_DEF%	FIGHT_ID	METHOD1
0	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	1.0	Asu Almabayev	0.0	4.0	0.0	0.0	3.233333	7.0	3.0	6.0	10.0	2.0	4.0	16.0	25.0	0.640000	23.0	34.0	0.676471	6.0	0.666667	15.0	0.466667	4.0	0.750000	6.0	1.0	19.0	0.526316	2.0	1.000000	4.0	1.0	NaN	NaN	NaN	None	None	NaN	NaN	None	None	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Win	NaN	CJ Vergara	65.577530	66.875164	28.567909	1.019928	66.226347	NaN	NaN	NaN	NaN	NaN	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	1.0	CJ Vergara	0.0	0.0	0.0	0.0	0.233333	3.0	3.0	1.0	4.0	3.0	0.0	7.0	14.0	0.500000	13.0	20.0	0.650000	0.0	NaN	9.0	0.333333	3.0	1.000000	2.0	0.5	11.0	0.363636	3.0	1.000000	0.0	NaN	66.0	135.0	68.0	Orthodox	1991-06-18	1.030303	67.0	CJ	Vergara	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Loss	32.0	Asu Almabayev	65.577530	66.875164	28.567909	1.019928	66.226347	0.422470	1.124836	3.432091	0.010375	0.773653	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	NaN	NaN	NaN	NaN	NaN	0.0	NaN	6.0	0.666667	0.0	0.000000	1.0	0.5	7.0	0.636364	0.0	0.000000	0.0	NaN	7.0	0.500000	7.0	0.350000	UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As...	Decision
1	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	2.0	Asu Almabayev	0.0	2.0	0.0	0.0	3.583333	10.0	1.0	1.0	4.0	1.0	7.0	12.0	21.0	0.571429	36.0	55.0	0.654545	2.0	1.000000	19.0	0.526316	1.0	1.000000	1.0	1.0	9.0	0.444444	2.0	0.500000	10.0	0.7	NaN	NaN	NaN	None	None	NaN	NaN	None	None	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Win	NaN	CJ Vergara	65.577530	66.875164	28.567909	1.019928	66.226347	NaN	NaN	NaN	NaN	NaN	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	2.0	CJ Vergara	0.0	0.0	0.0	0.0	0.000000	5.0	2.0	1.0	5.0	0.0	3.0	8.0	12.0	0.666667	25.0	30.0	0.833333	0.0	NaN	8.0	0.625000	3.0	0.666667	1.0	1.0	9.0	0.555556	0.0	NaN	3.0	1.0	66.0	135.0	68.0	Orthodox	1991-06-18	1.030303	67.0	CJ	Vergara	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Loss	32.0	Asu Almabayev	65.577530	66.875164	28.567909	1.019928	66.226347	0.422470	1.124836	3.432091	0.010375	0.773653	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	NaN	NaN	NaN	NaN	NaN	0.0	NaN	3.0	0.375000	1.0	0.333333	0.0	0.0	4.0	0.444444	0.0	NaN	0.0	0.0	4.0	0.333333	5.0	0.166667	UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As...	Decision
2	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	3.0	Asu Almabayev	0.0	3.0	0.0	0.0	2.716667	5.0	5.0	6.0	9.0	5.0	2.0	16.0	31.0	0.516129	26.0	43.0	0.604651	6.0	0.500000	19.0	0.263158	6.0	0.833333	6.0	1.0	23.0	0.391304	6.0	0.833333	2.0	1.0	NaN	NaN	NaN	None	None	NaN	NaN	None	None	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Win	NaN	CJ Vergara	65.577530	66.875164	28.567909	1.019928	66.226347	NaN	NaN	NaN	NaN	NaN	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	3.0	CJ Vergara	0.0	0.0	0.0	0.0	0.000000	5.0	9.0	0.0	13.0	1.0	0.0	14.0	27.0	0.518519	28.0	42.0	0.666667	0.0	NaN	15.0	0.333333	11.0	0.818182	1.0	0.0	26.0	0.500000	1.0	1.000000	0.0	NaN	66.0	135.0	68.0	Orthodox	1991-06-18	1.030303	67.0	CJ	Vergara	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Loss	32.0	Asu Almabayev	65.577530	66.875164	28.567909	1.019928	66.226347	0.422470	1.124836	3.432091	0.010375	0.773653	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	NaN	NaN	NaN	NaN	NaN	0.0	NaN	10.0	0.666667	2.0	0.181818	1.0	1.0	13.0	0.500000	0.0	0.000000	0.0	NaN	13.0	0.481481	14.0	0.333333	UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As...	Decision
3	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	1.0	CJ Vergara	0.0	0.0	0.0	0.0	0.233333	3.0	3.0	1.0	4.0	3.0	0.0	7.0	14.0	0.500000	13.0	20.0	0.650000	0.0	NaN	9.0	0.333333	3.0	1.000000	2.0	0.5	11.0	0.363636	3.0	1.000000	0.0	NaN	66.0	135.0	68.0	Orthodox	1991-06-18	1.030303	67.0	CJ	Vergara	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Loss	32.0	Asu Almabayev	65.577530	66.875164	28.567909	1.019928	66.226347	0.422470	1.124836	3.432091	0.010375	0.773653	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	1.0	Asu Almabayev	0.0	4.0	0.0	0.0	3.233333	7.0	3.0	6.0	10.0	2.0	4.0	16.0	25.0	0.640000	23.0	34.0	0.676471	6.0	0.666667	15.0	0.466667	4.0	0.750000	6.0	1.0	19.0	0.526316	2.0	1.000000	4.0	1.0	NaN	NaN	NaN	None	None	NaN	NaN	None	None	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Win	NaN	CJ Vergara	65.577530	66.875164	28.567909	1.019928	66.226347	NaN	NaN	NaN	NaN	NaN	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	NaN	NaN	NaN	NaN	NaN	2.0	0.333333	8.0	0.533333	1.0	0.250000	0.0	0.0	9.0	0.473684	0.0	0.000000	0.0	0.0	9.0	0.360000	11.0	0.323529	UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As...	Decision
4	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	2.0	CJ Vergara	0.0	0.0	0.0	0.0	0.000000	5.0	2.0	1.0	5.0	0.0	3.0	8.0	12.0	0.666667	25.0	30.0	0.833333	0.0	NaN	8.0	0.625000	3.0	0.666667	1.0	1.0	9.0	0.555556	0.0	NaN	3.0	1.0	66.0	135.0	68.0	Orthodox	1991-06-18	1.030303	67.0	CJ	Vergara	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Loss	32.0	Asu Almabayev	65.577530	66.875164	28.567909	1.019928	66.226347	0.422470	1.124836	3.432091	0.010375	0.773653	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	UFC 299: O'Malley vs. Vera 2	CJ Vergara vs. Asu Almabayev	2.0	Asu Almabayev	0.0	2.0	0.0	0.0	3.583333	10.0	1.0	1.0	4.0	1.0	7.0	12.0	21.0	0.571429	36.0	55.0	0.654545	2.0	1.000000	19.0	0.526316	1.0	1.000000	1.0	1.0	9.0	0.444444	2.0	0.500000	10.0	0.7	NaN	NaN	NaN	None	None	NaN	NaN	None	None	None	L/W	Flyweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	Keith Peterson	David Tirelli 27 - 30.MichaelTate 27 - 30.Sal ...	None	Men's	2024-03-09	Miami, Florida, USA	Miami	Florida	USA	14142904.0	NaN	Win	NaN	CJ Vergara	65.577530	66.875164	28.567909	1.019928	66.226347	NaN	NaN	NaN	NaN	NaN	UFC299:O'Malleyvs.Vera2-CJVergaravs.AsuAlmabay...	No	No	No	NaN	NaN	NaN	NaN	NaN	0.0	0.000000	9.0	0.473684	0.0	0.000000	0.0	0.0	5.0	0.555556	1.0	0.500000	3.0	0.3	9.0	0.428571	19.0	0.345455	UFC 299: O'Malley vs. Vera 2-CJ Vergara vs. As...	Decision
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34579	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	2.0	David Dodd	0.0	0.0	0.0	0.0	0.000000	1.0	2.0	0.0	0.0	2.0	1.0	3.0	5.0	0.600000	9.0	12.0	0.750000	0.0	NaN	3.0	0.333333	2.0	1.000000	0.0	NaN	2.0	0.000000	2.0	1.000000	1.0	1.0	74.0	200.0	74.0	Orthodox	1973-09-22	1.000000	74.0	David	Dodd	None	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Loss	26.0	Tyrone Roberts	72.668122	74.782579	30.365562	1.029232	73.725350	1.331878	-0.782579	-4.365562	-0.029232	0.274650	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	2.0	Tyrone Roberts	0.0	1.0	0.0	0.0	1.533333	3.0	1.0	0.0	2.0	2.0	0.0	4.0	14.0	0.285714	36.0	50.0	0.720000	1.0	1.000000	13.0	0.230769	1.0	1.000000	0.0	NaN	8.0	0.250000	6.0	0.333333	0.0	NaN	69.0	185.0	69.0	Orthodox	None	1.000000	69.0	Tyrone	Roberts	Native Warrior	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Win	NaN	David Dodd	72.668122	74.782579	30.365562	1.029232	73.725350	-3.668122	-5.782579	NaN	-0.029232	-4.725350	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	5.0	5.0	NaN	0.0	5.0	0.0	0.000000	10.0	0.769231	0.0	0.000000	0.0	NaN	6.0	0.750000	4.0	0.666667	0.0	NaN	10.0	0.714286	14.0	0.280000	UFC 26: Ultimate Field Of Dreams-Tyrone Robert...	Decision
34580	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	3.0	David Dodd	0.0	0.0	0.0	0.0	0.033333	0.0	3.0	0.0	0.0	3.0	0.0	3.0	13.0	0.230769	3.0	13.0	0.230769	1.0	0.000000	9.0	0.000000	4.0	0.750000	0.0	NaN	9.0	0.000000	4.0	0.750000	0.0	NaN	74.0	200.0	74.0	Orthodox	1973-09-22	1.000000	74.0	David	Dodd	None	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Loss	26.0	Tyrone Roberts	72.668122	74.782579	30.365562	1.029232	73.725350	1.331878	-0.782579	-4.365562	-0.029232	0.274650	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	3.0	Tyrone Roberts	0.0	0.0	0.0	0.0	0.033333	5.0	1.0	0.0	4.0	2.0	0.0	6.0	30.0	0.200000	8.0	32.0	0.250000	0.0	NaN	27.0	0.185185	3.0	0.333333	0.0	NaN	23.0	0.173913	7.0	0.285714	0.0	NaN	69.0	185.0	69.0	Orthodox	None	1.000000	69.0	Tyrone	Roberts	Native Warrior	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Win	NaN	David Dodd	72.668122	74.782579	30.365562	1.029232	73.725350	-3.668122	-5.782579	NaN	-0.029232	-4.725350	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	5.0	5.0	NaN	0.0	5.0	0.0	NaN	22.0	0.814815	2.0	0.666667	0.0	NaN	19.0	0.826087	5.0	0.714286	0.0	NaN	24.0	0.800000	24.0	0.750000	UFC 26: Ultimate Field Of Dreams-Tyrone Robert...	Decision
34581	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	1.0	Tyrone Roberts	0.0	0.0	0.0	0.0	3.216667	3.0	1.0	2.0	6.0	0.0	0.0	6.0	16.0	0.375000	61.0	81.0	0.753086	0.0	NaN	13.0	0.230769	1.0	1.000000	2.0	1.0	14.0	0.428571	0.0	NaN	2.0	0.0	69.0	185.0	69.0	Orthodox	None	1.000000	69.0	Tyrone	Roberts	Native Warrior	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Win	NaN	David Dodd	72.668122	74.782579	30.365562	1.029232	73.725350	-3.668122	-5.782579	NaN	-0.029232	-4.725350	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	1.0	David Dodd	0.0	0.0	0.0	0.0	0.000000	4.0	0.0	0.0	3.0	0.0	1.0	4.0	13.0	0.307692	15.0	24.0	0.625000	1.0	0.000000	13.0	0.307692	0.0	NaN	0.0	NaN	11.0	0.272727	0.0	NaN	2.0	0.5	74.0	200.0	74.0	Orthodox	1973-09-22	1.000000	74.0	David	Dodd	None	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Loss	26.0	Tyrone Roberts	72.668122	74.782579	30.365562	1.029232	73.725350	1.331878	-0.782579	-4.365562	-0.029232	0.274650	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	-5.0	-5.0	NaN	0.0	-5.0	1.0	1.000000	9.0	0.692308	0.0	NaN	0.0	NaN	8.0	0.727273	0.0	NaN	1.0	0.5	9.0	0.692308	9.0	0.375000	UFC 26: Ultimate Field Of Dreams-Tyrone Robert...	Decision
34582	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	2.0	Tyrone Roberts	0.0	1.0	0.0	0.0	1.533333	3.0	1.0	0.0	2.0	2.0	0.0	4.0	14.0	0.285714	36.0	50.0	0.720000	1.0	1.000000	13.0	0.230769	1.0	1.000000	0.0	NaN	8.0	0.250000	6.0	0.333333	0.0	NaN	69.0	185.0	69.0	Orthodox	None	1.000000	69.0	Tyrone	Roberts	Native Warrior	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Win	NaN	David Dodd	72.668122	74.782579	30.365562	1.029232	73.725350	-3.668122	-5.782579	NaN	-0.029232	-4.725350	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	2.0	David Dodd	0.0	0.0	0.0	0.0	0.000000	1.0	2.0	0.0	0.0	2.0	1.0	3.0	5.0	0.600000	9.0	12.0	0.750000	0.0	NaN	3.0	0.333333	2.0	1.000000	0.0	NaN	2.0	0.000000	2.0	1.000000	1.0	1.0	74.0	200.0	74.0	Orthodox	1973-09-22	1.000000	74.0	David	Dodd	None	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Loss	26.0	Tyrone Roberts	72.668122	74.782579	30.365562	1.029232	73.725350	1.331878	-0.782579	-4.365562	-0.029232	0.274650	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	-5.0	-5.0	NaN	0.0	-5.0	0.0	NaN	2.0	0.666667	0.0	0.000000	0.0	NaN	2.0	1.000000	0.0	0.000000	0.0	0.0	2.0	0.400000	3.0	0.250000	UFC 26: Ultimate Field Of Dreams-Tyrone Robert...	Decision
34583	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	3.0	Tyrone Roberts	0.0	0.0	0.0	0.0	0.033333	5.0	1.0	0.0	4.0	2.0	0.0	6.0	30.0	0.200000	8.0	32.0	0.250000	0.0	NaN	27.0	0.185185	3.0	0.333333	0.0	NaN	23.0	0.173913	7.0	0.285714	0.0	NaN	69.0	185.0	69.0	Orthodox	None	1.000000	69.0	Tyrone	Roberts	Native Warrior	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Win	NaN	David Dodd	72.668122	74.782579	30.365562	1.029232	73.725350	-3.668122	-5.782579	NaN	-0.029232	-4.725350	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	UFC 26: Ultimate Field Of Dreams	Tyrone Roberts vs. David Dodd	3.0	David Dodd	0.0	0.0	0.0	0.0	0.033333	0.0	3.0	0.0	0.0	3.0	0.0	3.0	13.0	0.230769	3.0	13.0	0.230769	1.0	0.000000	9.0	0.000000	4.0	0.750000	0.0	NaN	9.0	0.000000	4.0	0.750000	0.0	NaN	74.0	200.0	74.0	Orthodox	1973-09-22	1.000000	74.0	David	Dodd	None	W/L	Middleweight	Decision - Unanimous	3.0	5.0	3 Rnd (5-5-5)	John McCarthy	None	None	Men's	2000-06-09	Cedar Rapids, Iowa, USA	Cedar Rapids	Iowa	USA	NaN	NaN	Loss	26.0	Tyrone Roberts	72.668122	74.782579	30.365562	1.029232	73.725350	1.331878	-0.782579	-4.365562	-0.029232	0.274650	UFC26:UltimateFieldOfDreams-TyroneRobertsvs.Da...	No	No	No	-5.0	-5.0	NaN	0.0	-5.0	1.0	1.000000	9.0	1.000000	1.0	0.250000	0.0	NaN	9.0	1.000000	1.0	0.250000	0.0	NaN	10.0	0.769231	10.0	0.769231	UFC 26: Ultimate Field Of Dreams-Tyrone Robert...	Decision

	FIGHTER	STANCE	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	EITHER_CHAMP	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	Buyrate_PERCENTILE	PPV_Fights_PERCENTILE
0	Conor McGregor	Southpaw	Yes	Yes	Yes	Yes	13555166.0	1.355517e+06	10.0	100.000000	94.283647
1	Georges St-Pierre	Orthodox	Yes	Yes	Yes	Yes	12430000.0	5.650000e+05	22.0	99.956841	99.927641
2	Jim Miller	Southpaw	No	No	No	No	11741000.0	6.522778e+05	18.0	99.913681	99.493488
3	Jon Jones	Orthodox	Yes	Yes	Yes	Yes	10992000.0	6.465882e+05	17.0	99.870522	99.204052
4	Demian Maia	Southpaw	No	No	Yes	No	10907000.0	5.453500e+05	20.0	99.827363	99.710564
...	...	...	...	...	...	...	...	...	...	...	...
2312	Roque Martinez	Orthodox	No	No	No	No	0.0	NaN	NaN	20.198533	NaN
2313	Lara Procopio	Orthodox	No	No	No	No	0.0	NaN	NaN	20.198533	NaN
2314	Justin Jaynes	Orthodox	No	No	No	No	0.0	NaN	NaN	20.198533	NaN
2315	Montserrat Conejo	Southpaw	No	No	No	No	0.0	NaN	NaN	20.198533	NaN
2316	Tyrone Roberts	Orthodox	No	No	No	No	0.0	NaN	NaN	20.198533	NaN

	FIGHTER	STANCE	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	EITHER_CHAMP	KD	TD	SUB.ATT	REV	CTRL	HEAD	BODY	LEG	DISTANCE	CLINCH	GROUND	SIG	TOT.SIG	STR	TOTAL.STR	TOTAL.TD	TOTAL.HEAD	TOTAL.BODY	TOTAL.LEG	TOTAL.DISTANCE	TOTAL.CLINCH	TOTAL.GROUND	OPP_KD	OPP_TD	OPP_SUB.ATT	OPP_REV	OPP_CTRL	OPP_HEAD	OPP_BODY	OPP_LEG	OPP_DISTANCE	OPP_CLINCH	OPP_GROUND	OPP_SIG	OPP_TOT.SIG	OPP_STR	OPP_TOTAL.STR	OPP_TOTAL.TD	OPP_TOTAL.HEAD	OPP_TOTAL.BODY	OPP_TOTAL.LEG	OPP_TOTAL.DISTANCE	OPP_TOTAL.CLINCH	OPP_TOTAL.GROUND	TD_DEF	HEAD_DEF	BODY_DEF	LEG_DEF	DISTANCE_DEF	CLINCH_DEF	GROUND_DEF	SIG_DEF	STR_DEF	TD_%	HEAD_%	BODY_%	LEG_%	DISTANCE_%	CLINCH_%	GROUND_%	SIG_%	STR_%	TD_DEF_%	HEAD_DEF_%	BODY_DEF_%	LEG_DEF_%	DISTANCE_DEF_%	CLINCH_DEF_%	GROUND_DEF_%	SIG_DEF_%	STR_DEF_%	KD_PERCENTILE	TD_PERCENTILE	SUB.ATT_PERCENTILE	REV_PERCENTILE	CTRL_PERCENTILE	HEAD_PERCENTILE	BODY_PERCENTILE	LEG_PERCENTILE	DISTANCE_PERCENTILE	CLINCH_PERCENTILE	GROUND_PERCENTILE	SIG_PERCENTILE	STR_PERCENTILE	STR_%_PERCENTILE	TD_DEF_PERCENTILE	HEAD_DEF_PERCENTILE	BODY_DEF_PERCENTILE	LEG_DEF_PERCENTILE	DISTANCE_DEF_PERCENTILE	CLINCH_DEF_PERCENTILE	GROUND_DEF_PERCENTILE	SIG_DEF_PERCENTILE	STR_DEF_PERCENTILE
0	Asu Almabayev	None	No	No	No	No	0.0	9.0	0.0	0.0	9.533333	22.0	9.0	13.0	23.0	8.0	13.0	44.0	77.0	85.0	132.0	14.0	53.0	11.0	13.0	51.0	10.0	16.0	0.0	0.0	0.0	0.0	0.233333	13.0	14.0	2.0	22.0	4.0	3.0	29.0	53.0	66.0	92.0	0.0	32.0	17.0	4.0	46.0	4.0	3.0	0.0	19.0	3.0	2.0	24.0	0.0	0.0	24.0	26.0	0.642857	0.415094	0.818182	1.000000	0.450980	0.800000	0.812500	0.571429	0.643939	NaN	0.593750	0.176471	0.500000	0.521739	0.000000	0.000000	0.452830	0.282609	26.888218	76.866638	21.255934	29.477773	57.833405	22.852827	26.866638	41.583945	21.709107	33.987915	49.482089	24.492879	27.492447	83.506045	7.466552	9.861890	23.327579	35.930082	15.235218	5.955978	6.689685	10.876133	10.142426
1	CJ Vergara	Orthodox	No	No	No	No	0.0	0.0	1.0	1.0	10.750000	203.0	149.0	52.0	285.0	35.0	84.0	404.0	700.0	579.0	895.0	0.0	448.0	193.0	59.0	531.0	46.0	123.0	2.0	16.0	5.0	2.0	20.200000	222.0	91.0	49.0	314.0	24.0	24.0	362.0	762.0	438.0	858.0	40.0	588.0	121.0	53.0	699.0	32.0	31.0	24.0	366.0	30.0	4.0	385.0	8.0	7.0	400.0	420.0	NaN	0.453125	0.772021	0.881356	0.536723	0.760870	0.682927	0.577143	0.646927	0.600000	0.622449	0.247934	0.075472	0.550787	0.250000	0.225806	0.524934	0.489510	26.888218	11.588261	51.143720	69.486405	60.595598	76.391886	93.029780	77.427708	82.088908	70.716444	89.512300	82.239965	81.635736	83.894646	85.304273	76.931377	76.413466	48.640483	78.161416	50.107898	45.986189	76.413466	76.132931
2	Curtis Blaydes	Orthodox	No	No	No	No	2.0	62.0	0.0	0.0	80.250000	395.0	69.0	110.0	260.0	73.0	241.0	574.0	1144.0	1016.0	1681.0	116.0	941.0	80.0	123.0	754.0	91.0	299.0	4.0	13.0	2.0	0.0	7.650000	224.0	47.0	27.0	242.0	41.0	15.0	298.0	739.0	530.0	985.0	19.0	640.0	67.0	32.0	668.0	52.0	19.0	6.0	416.0	20.0	5.0	426.0	11.0	4.0	441.0	455.0	0.534483	0.419766	0.862500	0.894309	0.344828	0.802198	0.806020	0.501748	0.604402	0.315789	0.650000	0.298507	0.156250	0.637725	0.211538	0.210526	0.596752	0.461929	76.542943	99.525248	21.255934	29.477773	98.575744	90.504963	77.924040	91.735002	79.909366	87.915408	99.050496	89.490721	92.706085	74.265976	50.841606	79.844627	64.911524	53.582218	80.146741	59.710833	33.448425	78.700906	78.485110
3	Jailton Almeida	Orthodox	No	No	No	No	0.0	25.0	8.0	1.0	47.950000	143.0	8.0	2.0	8.0	3.0	142.0	153.0	232.0	328.0	442.0	42.0	216.0	10.0	6.0	22.0	8.0	202.0	0.0	0.0	0.0	0.0	1.100000	34.0	3.0	1.0	4.0	1.0	33.0	38.0	68.0	74.0	115.0	2.0	61.0	6.0	1.0	19.0	1.0	48.0	2.0	27.0	3.0	0.0	15.0	0.0	15.0	30.0	41.0	0.595238	0.662037	0.800000	0.333333	0.363636	0.375000	0.702970	0.659483	0.742081	1.000000	0.442623	0.500000	0.000000	0.789474	0.000000	0.312500	0.441176	0.356522	26.888218	94.238239	91.562365	69.486405	94.216659	65.947346	24.622356	14.436772	11.631420	18.644799	96.266724	55.761761	64.954683	95.293610	28.226155	13.508848	23.327579	9.387139	11.242987	5.955978	68.558481	13.487268	15.537333
4	Benoit Saint Denis	Southpaw	No	No	No	No	4.0	16.0	5.0	1.0	24.416667	159.0	85.0	43.0	166.0	50.0	71.0	287.0	525.0	439.0	700.0	43.0	372.0	108.0	45.0	335.0	64.0	126.0	1.0	4.0	6.0	0.0	3.666667	159.0	67.0	25.0	193.0	50.0	8.0	251.0	439.0	299.0	497.0	13.0	336.0	76.0	27.0	360.0	71.0	8.0	9.0	177.0	9.0	2.0	167.0	21.0	0.0	188.0	198.0	0.372093	0.427419	0.787037	0.955556	0.495522	0.781250	0.563492	0.546667	0.627143	0.692308	0.526786	0.118421	0.074074	0.463889	0.295775	0.000000	0.428246	0.398390	89.404402	88.044886	83.707380	69.486405	81.614156	69.249029	82.585240	72.766508	67.695296	79.499353	86.944325	72.701770	73.176521	80.440415	61.609840	54.963315	44.993526	35.930082	54.294346	77.470868	6.689685	53.970652	53.172205
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2312	Joao Roque	Orthodox	No	No	No	No	0.0	1.0	0.0	0.0	0.266667	2.0	2.0	1.0	5.0	0.0	0.0	5.0	59.0	5.0	59.0	18.0	43.0	6.0	10.0	57.0	1.0	1.0	0.0	0.0	0.0	1.0	2.216667	4.0	0.0	0.0	3.0	1.0	0.0	4.0	84.0	18.0	98.0	0.0	84.0	0.0	0.0	77.0	1.0	6.0	0.0	80.0	0.0	0.0	74.0	0.0	6.0	80.0	80.0	0.055556	0.046512	0.333333	0.100000	0.087719	0.000000	0.000000	0.084746	0.084746	NaN	0.952381	NaN	NaN	0.961039	0.000000	1.000000	0.952381	0.816327	26.888218	29.736729	21.255934	29.477773	9.667674	5.049633	10.703496	9.451877	8.782909	4.294346	8.610272	5.114372	3.064307	0.604491	7.466552	33.275788	4.661200	9.387139	33.664221	5.955978	41.929219	30.621493	27.880880
2313	Marcelo Aguiar	Orthodox	No	No	No	No	0.0	0.0	0.0	0.0	0.000000	2.0	0.0	0.0	0.0	0.0	2.0	2.0	2.0	21.0	21.0	0.0	2.0	0.0	0.0	0.0	0.0	2.0	0.0	2.0	0.0	0.0	4.133333	14.0	4.0	0.0	0.0	0.0	18.0	18.0	31.0	29.0	42.0	3.0	27.0	4.0	0.0	2.0	0.0	29.0	1.0	13.0	0.0	0.0	2.0	0.0	11.0	13.0	13.0	NaN	1.000000	NaN	NaN	NaN	NaN	1.000000	1.000000	1.000000	0.333333	0.481481	0.000000	NaN	1.000000	NaN	0.379310	0.419355	0.309524	26.888218	11.588261	21.255934	29.477773	2.546396	5.049633	2.740613	3.366422	1.143720	4.294346	25.097108	2.675874	8.718170	99.827288	19.615883	7.034959	4.661200	9.387139	2.481657	5.955978	58.761329	6.258092	5.157531
2314	Adrian Serrano	None	No	No	No	No	0.0	0.0	0.0	1.0	3.116667	2.0	4.0	0.0	3.0	3.0	0.0	6.0	23.0	55.0	79.0	2.0	16.0	7.0	0.0	15.0	8.0	0.0	0.0	3.0	0.0	0.0	2.000000	15.0	5.0	6.0	21.0	3.0	2.0	26.0	55.0	51.0	85.0	6.0	41.0	7.0	7.0	44.0	5.0	6.0	3.0	26.0	2.0	1.0	23.0	2.0	4.0	29.0	34.0	0.000000	0.125000	0.571429	NaN	0.200000	0.375000	NaN	0.260870	0.696203	0.500000	0.634146	0.285714	0.142857	0.522727	0.400000	0.666667	0.527273	0.400000	26.888218	11.588261	21.255934	69.486405	31.160984	5.049633	15.645231	3.366422	6.560207	18.644799	8.610272	5.848079	19.443246	90.932642	35.261114	12.947777	18.277946	25.377644	14.889944	22.442814	33.448425	12.990937	13.163574
2315	David Dodd	Orthodox	No	No	No	No	0.0	0.0	0.0	0.0	0.033333	5.0	5.0	0.0	3.0	5.0	2.0	10.0	31.0	27.0	49.0	2.0	25.0	6.0	0.0	22.0	6.0	3.0	0.0	1.0	0.0	0.0	4.783333	11.0	3.0	2.0	12.0	4.0	0.0	16.0	60.0	105.0	163.0	1.0	53.0	5.0	2.0	45.0	13.0	2.0	0.0	42.0	2.0	0.0	33.0	9.0	2.0	44.0	58.0	0.000000	0.200000	0.833333	NaN	0.136364	0.833333	0.666667	0.322581	0.551020	0.000000	0.792453	0.400000	0.000000	0.733333	0.692308	1.000000	0.733333	0.355828	26.888218	11.588261	21.255934	29.477773	5.761761	8.610272	17.716875	3.366422	6.560207	25.312905	25.097108	8.394476	10.681916	56.563040	7.466552	19.594303	18.277946	9.387139	18.536901	53.323263	22.874407	18.536901	20.759603
2316	Tyrone Roberts	Orthodox	No	No	No	No	0.0	1.0	0.0	0.0	4.783333	11.0	3.0	2.0	12.0	4.0	0.0	16.0	60.0	105.0	163.0	1.0	53.0	5.0	2.0	45.0	13.0	2.0	0.0	0.0	0.0	0.0	0.033333	5.0	5.0	0.0	3.0	5.0	2.0	10.0	31.0	27.0	49.0	2.0	25.0	6.0	0.0	22.0	6.0	3.0	2.0	20.0	1.0	0.0	19.0	1.0	1.0	21.0	22.0	1.000000	0.207547	0.600000	1.000000	0.266667	0.307692	0.000000	0.266667	0.644172	1.000000	0.800000	0.166667	NaN	0.863636	0.166667	0.333333	0.677419	0.448980	26.888218	29.736729	21.255934	29.477773	39.879154	13.875701	13.465688	14.436772	14.954683	22.183858	8.610272	11.631420	32.801036	83.549223	28.226155	10.336642	12.473025	9.387139	12.947777	15.580492	16.508416	9.883470	8.696590

	STANCE	RESULT	OPP_STANCE
14	Orthodox	Loss	Switch
15	Orthodox	Loss	Switch
16	Orthodox	Loss	Switch
17	Switch	Win	Orthodox
18	Switch	Win	Orthodox
...	...	...	...
34555	Orthodox	Loss	Southpaw
34556	Orthodox	Loss	Southpaw
34557	Orthodox	Loss	Southpaw
34568	Orthodox	Loss	Switch
34569	Switch	Win	Orthodox

	BOUT	RESULT	FRAME_DELTA	ADVANTAGE
6	Curtis Blaydes vs. Jailton Almeida	Win	1.0	Yes
8	Curtis Blaydes vs. Jailton Almeida	Loss	-1.0	No
10	Dustin Poirier vs. Benoit Saint Denis	Loss	1.5	Yes
12	Dustin Poirier vs. Benoit Saint Denis	Win	-1.5	No
14	Gilbert Burns vs. Jack Della Maddalena	Loss	-1.5	No
...	...	...	...	...
34572	Pat Miletich vs. John Alessio	Win	-1.0	No
34574	Shonie Carter vs. Adrian Serrano	Loss	-2.0	No
34576	Shonie Carter vs. Adrian Serrano	Win	2.0	Yes
34578	Tyrone Roberts vs. David Dodd	Loss	5.0	Yes
34581	Tyrone Roberts vs. David Dodd	Win	-5.0	No

	AGE
count	14141.000000
mean	29.780567
std	4.134685
min	18.000000
25%	27.000000
50%	30.000000
75%	32.000000
max	47.000000

	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	Buyrate_PERCENTILE	PPV_Fights_PERCENTILE
count	1.382000e+03	1.382000e+03	1382.000000	1382.000000	1382.000000
mean	1.574689e+06	4.843432e+05	3.251085	70.198533	50.036179
std	1.796960e+06	3.025672e+05	3.343249	17.223849	27.769294
min	3.500000e+04	3.500000e+04	1.000000	40.483384	20.260492
25%	4.000000e+05	2.904444e+05	1.000000	55.135952	20.260492
50%	9.150000e+05	4.400000e+05	2.000000	70.198533	49.963821
75%	2.040000e+06	6.150000e+05	4.000000	85.088476	74.963821
max	1.355517e+07	2.400000e+06	22.000000	100.000000	99.927641

	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	Buyrate_PERCENTILE	PPV_Fights_PERCENTILE
Buyrate	1.000000	0.314592	0.894954	0.816858	0.744467
Avg_Buyrate_Per_Event	0.314592	1.000000	0.000047	0.532203	-0.001989
PPV_Fights	0.894954	0.000047	1.000000	0.717016	0.825409
Buyrate_PERCENTILE	0.816858	0.532203	0.717016	1.000000	0.812387
PPV_Fights_PERCENTILE	0.744467	-0.001989	0.825409	0.812387	1.000000

	STANCE	Buyrate	Percentage
0	Orthodox	1.643317e+09	75.512465
1	Southpaw	4.469722e+08	20.538929
2	Switch	8.104633e+07	3.724180

	mean	median
STANCE
Orthodox	1.538686e+06	892500.0
Southpaw	1.787889e+06	1000000.0
Switch	1.500858e+06	1245000.0

	EITHER_CHAMP	Buyrate	Percentage
0	No	1.750347e+09	80.430609
1	Yes	4.258729e+08	19.569391

	EVER_CHALLENGER	Buyrate	Percentage
0	No	1.435586e+09	65.966973
1	Yes	7.406334e+08	34.033027

	mean	median
EITHER_CHAMP
No	1.366391e+06	840000.0
Yes	4.216564e+06	3713000.0

	mean	median
EITHER_CHAMP
No	68.805678	68.580060
Yes	87.864343	93.655589

	mean	median
EVER_CHALLENGER
No	1.226997e+06	787500.0
Yes	3.493554e+06	2789500.0

	group1	group2	meandiff	p-adj	lower	upper	reject
0	Orthodox	Southpaw	249202.4272	0.1198	-47447.6301	545852.4844	False
1	Orthodox	Switch	-37828.4288	0.9876	-626748.4028	551091.5451	False
2	Southpaw	Switch	-287030.8560	0.5373	-920626.6266	346564.9146	False

	EVER_CHALLENGER_BINARY	Buyrate
EVER_CHALLENGER_BINARY	1.000000	0.454714
Buyrate	0.454714	1.000000

	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	EITHER_CHAMP_dummy	EVER_CHALLENGER_dummy	STANCE_Orthodox	STANCE_Southpaw	STANCE_Switch
Buyrate	1.000000	0.049096	0.685557	0.308537	0.292038	-0.268087	0.302463	-0.066884
Avg_Buyrate_Per_Event	0.049096	1.000000	-0.584288	-0.097268	-0.137815	-0.024369	-0.027014	0.144238
PPV_Fights	0.685557	-0.584288	1.000000	0.366633	0.362326	-0.224168	0.233117	-0.001336
EITHER_CHAMP_dummy	0.308537	-0.097268	0.366633	1.000000	0.649108	-0.016195	0.017633	-0.002279
EVER_CHALLENGER_dummy	0.292038	-0.137815	0.362326	0.649108	1.000000	-0.067283	0.093448	-0.065150
STANCE_Orthodox	-0.268087	-0.024369	-0.224168	-0.016195	-0.067283	1.000000	-0.937333	-0.276952
STANCE_Southpaw	0.302463	-0.027014	0.233117	0.017633	0.093448	-0.937333	1.000000	-0.075210
STANCE_Switch	-0.066884	0.144238	-0.001336	-0.002279	-0.065150	-0.276952	-0.075210	1.000000

	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	EITHER_CHAMP_dummy	EVER_CHALLENGER_dummy	STANCE_Orthodox	STANCE_Southpaw
Buyrate	1.000000	0.589853	0.113339	0.357520	0.034371	-0.164151	0.164151
Avg_Buyrate_Per_Event	0.589853	1.000000	-0.704153	0.158545	0.023418	-0.124390	0.124390
PPV_Fights	0.113339	-0.704153	1.000000	0.204082	0.093993	-0.083649	0.083649
EITHER_CHAMP_dummy	0.357520	0.158545	0.204082	1.000000	0.460566	-0.219578	0.219578
EVER_CHALLENGER_dummy	0.034371	0.023418	0.093993	0.460566	1.000000	-0.101130	0.101130
STANCE_Orthodox	-0.164151	-0.124390	-0.083649	-0.219578	-0.101130	1.000000	-1.000000
STANCE_Southpaw	0.164151	0.124390	0.083649	0.219578	0.101130	-1.000000	1.000000

	HEIGHT	WEIGHT	REACH	APE_INDEX	FRAME	HEIGHT_DIFF	REACH_DIFF	AGE_DIFF	AI_DIFF	FRAME_DIFF	OPP_HEIGHT	OPP_WEIGHT	OPP_REACH	OPP_APE_INDEX	OPP_FRAME	OPP_REACH_DIFF	OPP_AGE_DIFF	OPP_AI_DIFF	OPP_FRAME_DIFF	HEIGHT_DELTA	REACH_DELTA	AGE_DELTA	AI_DELTA	FRAME_DELTA	HEIGHT_DELTA_RANK	REACH_DELTA_RANK	AGE_DELTA_RANK	AI_DELTA_RANK	FRAME_DELTA_RANK	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
count	2180.000000	2285.000000	2180.000000	2180.000000	2180.000000	2180.000000	2180.000000	2221.000000	2180.000000	2180.000000	2278.000000	2310.000000	2278.000000	2278.000000	2278.000000	2278.000000	2297.000000	2278.000000	2278.000000	2173.000000	2173.000000	2206.000000	2173.000000	2173.000000	2173.000000	2173.000000	2206.000000	2173.000000	2173.000000	2173.000000	2173.000000	2173.000000
mean	70.160092	166.136543	71.490826	1.018953	70.825459	-0.058956	-0.466490	-0.564307	-0.005690	-0.262723	70.193045	165.988108	71.830934	1.023263	71.011989	-0.056961	-0.636821	-0.001250	-0.011852	-0.083528	-0.399578	0.056264	-0.004412	-0.241553	241.616659	285.902899	303.892112	986.327197	332.572941	50.023010	50.023010	50.023010
std	3.482015	34.890644	4.011389	0.026378	3.640943	1.942093	2.564479	3.548923	0.026326	2.080214	3.148040	34.201223	3.642067	0.017115	3.350022	1.563383	2.566301	0.016283	1.252395	2.077244	2.734528	4.109363	0.029627	2.198091	124.078774	140.424174	165.529317	542.493323	166.551398	28.864216	28.867396	28.871629
min	60.000000	115.000000	58.000000	0.920635	60.000000	-9.328423	-11.251590	-11.267538	-0.082762	-10.290007	60.000000	115.000000	60.000000	0.946667	60.000000	-7.251590	-11.267538	-0.082565	-5.665245	-7.000000	-13.000000	-13.000000	-0.132219	-8.500000	1.000000	1.000000	1.000000	1.000000	1.000000	0.115048	0.046019	0.069029
25%	68.000000	145.000000	69.000000	1.000000	68.500000	-1.328423	-2.185220	-3.101245	-0.027023	-1.592139	68.000000	140.000000	69.381250	1.013699	68.666667	-0.875164	-2.125546	-0.010501	-0.671204	-1.500000	-2.000000	-2.980769	-0.024443	-1.600000	141.000000	173.000000	166.000000	539.000000	198.000000	23.815002	27.105384	24.988495
50%	70.000000	155.000000	72.000000	1.013889	71.000000	-0.088424	-0.501138	-0.685643	-0.010878	-0.290007	70.285714	158.215909	72.000000	1.023611	71.166667	0.013388	-0.434578	-0.001392	0.036262	0.000000	-0.333333	0.000000	-0.005616	-0.208333	232.000000	287.000000	301.000000	976.000000	338.000000	52.600092	50.253106	50.046019
75%	73.000000	185.000000	74.000000	1.039089	73.500000	1.170691	1.217421	1.897596	0.012026	1.108479	72.567460	185.000000	74.500000	1.032544	73.500000	0.765050	0.950849	0.007051	0.709993	1.166667	1.333333	2.700000	0.015002	1.131579	350.000000	398.000000	451.750000	1462.000000	476.000000	74.873447	74.666360	75.011505
max	83.000000	275.000000	84.000000	1.125000	83.500000	8.138963	8.720020	13.353224	0.101657	7.589222	79.500000	265.000000	82.000000	1.109375	80.250000	7.748410	15.172323	0.084828	5.709993	8.083333	10.333333	16.000000	0.106059	8.333333	467.000000	518.000000	610.000000	1916.000000	620.000000	100.000000	100.000000	100.000000

	FIGHTER	STANCE	EVER_CHAMPION	EVER_INTERIM	EVER_CHALLENGER	EITHER_CHAMP	APE_INDEX	HEIGHT_DELTA	REACH_DELTA	FRAME_DELTA	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
5	Dustin Poirier	Southpaw	No	Yes	Yes	Yes	1.043478	-0.733333	0.366667	-0.183333	37.689830	61.297745	50.529222
17	Rafael Dos Anjos	Southpaw	Yes	No	Yes	Yes	1.029412	-2.428571	-2.371429	-2.400000	11.803958	22.411413	15.485504
22	Petr Yan	Switch	Yes	Yes	Yes	Yes	1.000000	0.076923	-1.923077	-0.923077	55.384261	29.498389	37.735849
27	Sean O'Malley	Switch	Yes	No	Yes	Yes	1.014085	3.500000	2.916667	3.208333	96.042338	88.748274	94.845835
50	Brandon Moreno	Orthodox	Yes	Yes	Yes	Yes	1.044776	1.200000	2.266667	1.733333	75.218592	84.583525	82.236539
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2241	Carlos Newton	Orthodox	Yes	No	Yes	Yes	1.000000	-1.250000	-2.250000	-1.750000	27.979751	23.676944	23.101703
2246	Ricco Rodriguez	Orthodox	Yes	No	Yes	Yes	1.000000	2.285714	1.000000	1.642857	87.735849	70.432582	81.132075
2273	Murilo Bustamante	Orthodox	Yes	No	Yes	Yes	1.000000	2.000000	0.666667	1.333333	84.836631	66.037736	77.657616
2278	Pat Miletich	Orthodox	Yes	No	Yes	Yes	1.000000	-0.600000	-1.400000	-1.000000	40.128854	35.066728	35.687989
2282	Kevin Randleman	Orthodox	Yes	No	Yes	Yes	1.000000	-3.250000	-4.750000	-4.000000	5.338242	6.189600	4.555913

	APE_INDEX	HEIGHT_DELTA	REACH_DELTA	FRAME_DELTA	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
count	105.000000	105.000000	105.000000	105.000000	105.000000	105.000000	105.000000
mean	1.026797	0.056519	0.200751	0.128635	52.694103	56.344093	55.304933
std	0.027708	1.694103	2.395933	1.831165	25.876933	26.727779	26.141637
min	0.955882	-4.000000	-6.400000	-5.033333	2.922227	1.679705	1.702715
25%	1.000000	-0.812500	-1.400000	-1.000000	36.493327	35.066728	35.687989
50%	1.027027	0.000000	0.235294	0.095238	52.600092	58.950759	55.729406
75%	1.044776	1.166667	1.571429	1.500000	74.873447	77.542568	79.475380
max	1.105263	5.500000	8.260870	5.173913	99.332720	99.907961	99.171652

	APE_INDEX	HEIGHT_DELTA	REACH_DELTA	FRAME_DELTA	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
count	221.000000	221.000000	221.000000	221.000000	221.000000	221.000000	221.000000
mean	1.025508	-0.033799	0.107765	0.036983	50.925176	55.299094	53.820125
std	0.027757	1.797022	2.417903	1.889372	26.200253	26.819117	26.540393
min	0.955882	-6.571429	-7.388889	-6.583333	0.230097	0.690290	0.460193
25%	1.000000	-1.076923	-1.500000	-1.071429	29.682467	33.548090	33.617119
50%	1.027027	-0.041667	0.000000	0.000000	49.930971	55.453290	53.796595
75%	1.043478	1.000000	1.571429	1.312500	71.514036	77.542568	77.450529
max	1.120000	6.500000	8.260870	6.125000	99.769903	99.907961	99.654855

	APE_INDEX	HEIGHT_DELTA	REACH_DELTA	FRAME_DELTA	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
count	2075.000000	2068.000000	2068.000000	2068.000000	2068.000000	2068.000000	2068.000000
mean	1.018556	-0.090638	-0.430059	-0.260349	49.887388	49.702065	49.754827
std	0.026253	2.094919	2.747623	2.213824	29.006774	28.941008	28.983471
min	0.920635	-7.000000	-13.000000	-8.500000	0.115048	0.046019	0.069029
25%	1.000000	-1.500000	-2.090909	-1.625000	23.815002	24.643350	24.551312
50%	1.013889	-0.064583	-0.375000	-0.228220	49.804418	49.654855	49.689370
75%	1.031746	1.166667	1.333333	1.125000	74.873447	74.666360	74.827428
max	1.125000	8.083333	10.333333	8.333333	100.000000	100.000000	100.000000

Prologue¶

Import Libraries¶

Directory¶

Display Limit¶

Functions¶

SQL Start¶

Queries¶

master¶

fighter_buyrate¶

event_buyrate¶

frame¶

career¶

fight_average¶

round_average¶

records¶

Weight Classes¶

Analyses¶

Versus¶

Stances¶

Advantages¶

Age¶

Deltas¶

fighter_buyrate stats¶

fighter_buyrate correlations¶

frame stats¶

Champions¶

Challengers¶

Non Champions¶

Non Challengers¶

career_stats¶

Champions¶

Challengers¶

Non Champions¶

Non Challengers¶

fight_average¶

Champions¶

Challengers¶

Non Champions¶

Non Challengers¶

round_average¶

Champions¶

Challengers¶

Non Champions¶

Non Challengers¶

records_stats¶

Top 1 Percent¶

Top 10 Percent¶

Bottom 90 Percent¶

Champions¶

Challengers¶

Non Champions¶

Challengers¶

event_buyrate stats¶

Close SQL¶

Tests¶

Buyrate Tests¶

Stances¶

Champions¶

Challengers¶

Top 1 Percent¶

Top 10 Percent¶

Clustering¶

Records¶

Exploratory¶

Distributions¶

Statistical Experiments¶

Significance Testing¶

Top 80%¶

	APE_INDEX	HEIGHT_DELTA	REACH_DELTA	FRAME_DELTA	HEIGHT_DELTA_PERCENTILE	REACH_DELTA_PERCENTILE	FRAME_DELTA_PERCENTILE
count	1959.000000	1952.000000	1952.000000	1952.000000	1952.000000	1952.000000	1952.000000
mean	1.018213	-0.089158	-0.457019	-0.273088	49.920869	49.425666	49.593111
std	0.026122	2.106956	2.762785	2.228585	29.154905	29.036276	29.098911
min	0.920635	-7.000000	-13.000000	-8.500000	0.115048	0.046019	0.069029
25%	1.000000	-1.500000	-2.135714	-1.666667	23.815002	24.275196	24.160147
50%	1.013699	0.000000	-0.428571	-0.240385	52.600092	49.148642	49.378739
75%	1.031250	1.203571	1.333333	1.114583	75.247354	74.666360	74.689370
max	1.125000	8.083333	10.333333	8.333333	100.000000	100.000000	100.000000

	KD	TD	SUB.ATT	REV	CTRL	HEAD	BODY	LEG	DISTANCE	CLINCH	GROUND	SIG	TOT.SIG	STR	TOTAL.STR	TOTAL.TD	TOTAL.HEAD	TOTAL.BODY	TOTAL.LEG	TOTAL.DISTANCE	TOTAL.CLINCH	TOTAL.GROUND	OPP_KD	OPP_TD	OPP_SUB.ATT	OPP_REV	OPP_CTRL	OPP_HEAD	OPP_BODY	OPP_LEG	OPP_DISTANCE	OPP_CLINCH	OPP_GROUND	OPP_SIG	OPP_TOT.SIG	OPP_STR	OPP_TOTAL.STR	OPP_TOTAL.TD	OPP_TOTAL.HEAD	OPP_TOTAL.BODY	OPP_TOTAL.LEG	OPP_TOTAL.DISTANCE	OPP_TOTAL.CLINCH	OPP_TOTAL.GROUND	TD_DEF	HEAD_DEF	BODY_DEF	LEG_DEF	DISTANCE_DEF	CLINCH_DEF	GROUND_DEF	SIG_DEF	STR_DEF	TD_%	HEAD_%	BODY_%	LEG_%	DISTANCE_%	CLINCH_%	GROUND_%	SIG_%	STR_%	TD_DEF_%	HEAD_DEF_%	BODY_DEF_%	LEG_DEF_%	DISTANCE_DEF_%	CLINCH_DEF_%	GROUND_DEF_%	SIG_DEF_%	STR_DEF_%	KD_PERCENTILE	TD_PERCENTILE	SUB.ATT_PERCENTILE	REV_PERCENTILE	CTRL_PERCENTILE	HEAD_PERCENTILE	BODY_PERCENTILE	LEG_PERCENTILE	DISTANCE_PERCENTILE	CLINCH_PERCENTILE	GROUND_PERCENTILE	SIG_PERCENTILE	STR_PERCENTILE	STR_%_PERCENTILE	TD_DEF_PERCENTILE	HEAD_DEF_PERCENTILE	BODY_DEF_PERCENTILE	LEG_DEF_PERCENTILE	DISTANCE_DEF_PERCENTILE	CLINCH_DEF_PERCENTILE	GROUND_DEF_PERCENTILE	SIG_DEF_PERCENTILE	STR_DEF_PERCENTILE
count	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1289.000000	1321.000000	1321.000000	1314.000000	1321.000000	1318.000000	1308.000000	1321.000000	1321.000000	1319.000000	1321.000000	1320.000000	1321.000000	1321.000000	1315.000000	1308.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000	1321.000000
mean	2.252839	10.596518	3.781983	1.286147	22.030066	232.949281	75.190008	59.572294	266.233157	50.746404	50.732021	367.711582	824.610901	543.055261	1024.389856	27.595004	642.404996	108.452687	73.753217	678.369417	72.629069	73.612415	1.996972	9.981075	3.450416	1.267979	20.563096	221.841787	73.132475	58.787282	260.554126	48.726722	44.480696	353.761544	807.454958	519.335352	995.927328	27.336109	627.856927	106.520061	73.077971	672.462528	70.308857	64.683573	17.355034	406.015140	33.387585	14.290689	411.908403	21.582135	20.202877	453.693414	476.591976	0.391199	0.365860	0.701506	0.810749	0.385467	0.688273	0.686477	0.449923	0.537240	0.601871	0.632236	0.304079	0.188745	0.611299	0.307903	0.294140	0.547024	0.460158	62.648293	64.742954	61.812797	58.556462	66.622653	69.235339	67.773887	66.463378	68.158220	67.023174	67.203865	69.318571	69.562579	51.257519	66.023242	68.582298	67.197282	65.397808	68.244441	65.752476	61.479660	68.636533	68.778982
std	2.797936	12.094638	4.775982	1.735074	21.518480	206.074111	73.364291	64.177366	256.704216	51.291325	55.189324	318.010726	706.163668	437.079073	824.978180	30.025952	556.757267	105.465905	80.207654	621.034104	70.811884	79.410315	2.045688	8.668933	3.875037	1.741024	16.528213	177.951083	59.925306	53.229429	231.984711	43.066319	39.862831	274.672077	652.592225	362.823442	740.163124	23.091038	516.434098	88.937737	66.251352	591.595722	61.603952	59.203397	16.540045	348.218196	31.811799	15.154567	367.629078	20.419601	21.753746	387.552154	396.360506	0.199546	0.080352	0.109492	0.115944	0.079219	0.128892	0.141052	0.078441	0.091430	0.204568	0.081236	0.105665	0.102224	0.070787	0.116369	0.131679	0.073071	0.089467	26.283458	25.091221	26.854650	26.956676	23.168898	19.982243	21.640616	23.313468	21.324377	22.463853	22.174434	19.852317	19.548855	25.002859	23.569217	20.741931	22.517239	23.963256	21.204668	23.639517	26.330683	20.713706	20.568315
min	0.000000	0.000000	0.000000	0.000000	0.016667	3.000000	0.000000	0.000000	2.000000	0.000000	0.000000	4.000000	17.000000	20.000000	55.000000	0.000000	15.000000	2.000000	0.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.016667	8.000000	0.000000	1.000000	4.000000	0.000000	0.000000	16.000000	26.000000	48.000000	77.000000	0.000000	15.000000	0.000000	1.000000	11.000000	0.000000	0.000000	0.000000	7.000000	0.000000	0.000000	4.000000	0.000000	0.000000	10.000000	13.000000	0.000000	0.110497	0.000000	0.000000	0.076923	0.000000	0.000000	0.204167	0.263804	0.000000	0.283019	0.000000	0.000000	0.359375	0.000000	0.000000	0.269841	0.156823	26.888218	11.588261	21.255934	29.477773	5.308589	6.258092	2.740613	3.366422	4.984894	4.294346	8.610272	4.359085	8.416055	2.245250	7.466552	3.690117	4.661200	9.387139	4.380665	5.955978	6.689685	4.315926	5.157531
25%	0.000000	3.000000	1.000000	0.000000	7.500000	94.000000	27.000000	19.000000	96.000000	17.000000	15.000000	151.000000	337.000000	242.000000	446.000000	8.000000	260.000000	38.000000	24.000000	261.000000	26.000000	22.000000	0.000000	4.000000	1.000000	0.000000	9.433333	101.000000	31.000000	23.000000	103.000000	19.000000	16.000000	161.000000	359.000000	266.000000	493.000000	11.000000	273.000000	44.000000	29.000000	275.000000	27.000000	23.000000	6.000000	170.000000	12.000000	4.000000	164.000000	8.000000	6.000000	192.000000	210.000000	0.268657	0.313343	0.638158	0.754324	0.336842	0.622685	0.611896	0.399329	0.475073	0.481917	0.582979	0.238054	0.119048	0.567010	0.234043	0.213993	0.503226	0.404405	26.888218	49.352611	51.143720	29.477773	50.798446	55.394907	52.934830	51.100561	53.409581	51.402676	52.244281	55.351748	55.999137	30.310881	50.841606	54.229607	52.157963	48.640483	53.798015	50.107898	41.929219	54.251187	54.380665
50%	1.000000	7.000000	2.000000	1.000000	15.566667	172.000000	52.000000	39.000000	186.000000	35.000000	33.000000	272.000000	608.000000	416.000000	776.000000	18.000000	474.000000	77.000000	48.000000	484.000000	50.000000	48.000000	1.000000	8.000000	2.000000	1.000000	16.400000	166.000000	57.000000	43.000000	188.000000	36.000000	34.000000	268.000000	606.000000	411.000000	769.000000	21.000000	467.000000	83.000000	52.000000	488.000000	53.000000	48.000000	12.000000	301.000000	24.000000	10.000000	299.000000	15.000000	14.000000	334.000000	358.000000	0.380952	0.364055	0.702128	0.823529	0.385321	0.694680	0.693082	0.448454	0.536972	0.625000	0.637612	0.303436	0.180000	0.611111	0.304000	0.294118	0.552867	0.463816	62.580924	70.975399	65.105740	69.486405	70.608546	71.299094	70.608546	70.155373	70.975399	70.716444	70.414329	71.320673	71.342253	51.986183	69.227449	71.428571	69.917997	70.241692	71.126457	68.774277	66.055244	71.406992	71.471731
75%	3.000000	14.000000	5.000000	2.000000	30.366667	299.000000	98.000000	76.000000	337.000000	65.000000	66.000000	467.000000	1052.000000	700.000000	1328.000000	36.000000	838.000000	143.000000	94.000000	871.000000	93.000000	98.000000	3.000000	14.000000	5.000000	2.000000	26.716667	290.000000	96.000000	77.000000	338.000000	66.000000	58.000000	454.000000	1040.000000	678.000000	1273.000000	37.000000	820.000000	139.000000	96.000000	885.000000	97.000000	87.000000	24.000000	538.000000	44.000000	20.000000	548.000000	29.000000	27.000000	601.000000	622.000000	0.500000	0.416667	0.767857	0.883877	0.436813	0.772190	0.768897	0.499382	0.596290	0.740741	0.684669	0.370370	0.250000	0.658960	0.375000	0.375000	0.595300	0.522124	84.570565	85.476910	83.707380	84.613725	85.757445	85.779025	85.714286	85.843763	85.757445	85.951662	85.606388	85.735865	85.757445	71.718480	85.304273	85.800604	85.779025	85.973241	85.779025	85.520069	84.354769	85.757445	85.757445
max	20.000000	90.000000	48.000000	13.000000	162.116667	2117.000000	723.000000	508.000000	2709.000000	453.000000	461.000000	3122.000000	6586.000000	3366.000000	6859.000000	274.000000	5254.000000	964.000000	630.000000	6034.000000	576.000000	656.000000	12.000000	74.000000	29.000000	12.000000	128.183333	1310.000000	454.000000	486.000000	1930.000000	293.000000	273.000000	2086.000000	5151.000000	2460.000000	5276.000000	165.000000	4005.000000	770.000000	611.000000	4894.000000	437.000000	420.000000	126.000000	2695.000000	316.000000	128.000000	2964.000000	162.000000	163.000000	3065.000000	3085.000000	1.000000	0.769231	1.000000	1.000000	0.680769	1.000000	1.000000	0.797101	0.824593	1.000000	0.902913	1.000000	0.600000	0.856707	1.000000	1.000000	0.813889	0.758883	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	98.402418	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000

	KD	TD	SUB.ATT	REV	CTRL	HEAD	BODY	LEG	DISTANCE	CLINCH	GROUND	SIG	TOT.SIG	STR	TOTAL.STR	TOTAL.TD	TOTAL.HEAD	TOTAL.BODY	TOTAL.LEG	TOTAL.DISTANCE	TOTAL.CLINCH	TOTAL.GROUND	OPP_KD	OPP_TD	OPP_SUB.ATT	OPP_REV	OPP_CTRL	OPP_HEAD	OPP_BODY	OPP_LEG	OPP_DISTANCE	OPP_CLINCH	OPP_GROUND	OPP_SIG	OPP_TOT.SIG	OPP_STR	OPP_TOTAL.STR	OPP_TOTAL.TD	OPP_TOTAL.HEAD	OPP_TOTAL.BODY	OPP_TOTAL.LEG	OPP_TOTAL.DISTANCE	OPP_TOTAL.CLINCH	OPP_TOTAL.GROUND	TD_DEF	HEAD_DEF	BODY_DEF	LEG_DEF	DISTANCE_DEF	CLINCH_DEF	GROUND_DEF	SIG_DEF	STR_DEF	TD_%	HEAD_%	BODY_%	LEG_%	DISTANCE_%	CLINCH_%	GROUND_%	SIG_%	STR_%	TD_DEF_%	HEAD_DEF_%	BODY_DEF_%	LEG_DEF_%	DISTANCE_DEF_%	CLINCH_DEF_%	GROUND_DEF_%	SIG_DEF_%	STR_DEF_%	KD_PERCENTILE	TD_PERCENTILE	SUB.ATT_PERCENTILE	REV_PERCENTILE	CTRL_PERCENTILE	HEAD_PERCENTILE	BODY_PERCENTILE	LEG_PERCENTILE	DISTANCE_PERCENTILE	CLINCH_PERCENTILE	GROUND_PERCENTILE	SIG_PERCENTILE	STR_PERCENTILE	STR_%_PERCENTILE	TD_DEF_PERCENTILE	HEAD_DEF_PERCENTILE	BODY_DEF_PERCENTILE	LEG_DEF_PERCENTILE	DISTANCE_DEF_PERCENTILE	CLINCH_DEF_PERCENTILE	GROUND_DEF_PERCENTILE	SIG_DEF_PERCENTILE	STR_DEF_PERCENTILE
count	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	1981.000000	2201.000000	2124.000000	2086.000000	2203.000000	2071.000000	1872.000000	2208.000000	2211.000000	2025.000000	2206.000000	2142.000000	2083.000000	2202.000000	2080.000000	2057.000000	2209.000000	2210.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2211.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000	2212.000000
mean	1.196655	6.070976	2.240054	0.813743	12.705048	128.974231	42.659584	33.820524	149.303797	28.845389	27.305154	205.454340	469.159584	307.912749	586.511302	16.406872	364.967902	62.124322	42.067360	387.593580	41.674503	39.891501	1.302893	6.388336	2.350814	0.819620	13.348825	134.397830	43.890145	34.611664	153.214738	29.881103	29.803797	212.899638	476.619801	318.518083	597.440325	16.552893	370.669078	63.174051	42.776673	390.350362	42.892857	43.376582	10.164557	236.271248	19.283906	8.165009	237.135624	13.011754	13.572785	263.720163	278.922242	0.363704	0.342294	0.684252	0.794655	0.370481	0.666827	0.678273	0.434385	0.529423	0.556787	0.605819	0.289229	0.188687	0.605248	0.303750	0.296509	0.523505	0.433996	48.526771	48.625918	48.813511	49.415623	48.492938	48.284673	48.418170	48.544565	48.459281	48.390279	48.322955	48.301677	48.258801	49.504118	48.620338	48.444774	48.658736	48.634337	48.468510	48.665624	49.057735	48.448657	48.453905
std	2.095358	9.083109	3.758273	1.415374	16.608481	157.106067	55.884705	47.880570	193.425908	39.013347	40.027201	245.613603	557.823609	349.754395	666.956357	23.118124	438.394571	81.522317	59.498245	483.687484	54.430332	58.307874	1.704155	7.266505	3.212775	1.421714	13.975472	146.895591	49.493931	42.736809	187.128538	35.236359	33.248219	228.742736	532.003947	311.648691	618.567347	18.857710	419.168494	72.377284	52.438897	472.447079	50.206573	49.199110	12.924967	278.462782	24.680846	11.072369	290.644969	16.278886	17.756294	309.597892	319.274652	0.251893	0.121814	0.173993	0.172481	0.118986	0.194211	0.205022	0.119757	0.132194	0.260587	0.119046	0.155346	0.155529	0.112038	0.169598	0.167221	0.104861	0.118656	25.711523	28.176033	27.218654	25.300067	28.390934	28.168098	28.239593	28.335174	28.254334	28.287326	28.166084	28.164904	28.157668	29.027402	28.320259	28.301331	28.329383	28.200302	28.302099	28.401996	28.663245	28.295610	28.295533
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	26.888218	11.588261	21.255934	29.477773	2.546396	1.316357	2.740613	3.366422	1.143720	4.294346	8.610272	0.604230	0.258956	0.237478	7.466552	0.215796	4.661200	9.387139	0.388433	5.955978	6.689685	0.151057	0.107898
25%	0.000000	1.000000	0.000000	0.000000	1.933333	23.000000	8.000000	5.000000	26.000000	4.750000	2.000000	43.000000	105.750000	71.000000	144.000000	2.000000	77.000000	12.000000	6.000000	76.000000	7.750000	3.000000	0.000000	1.000000	0.000000	0.000000	3.550000	38.000000	10.000000	6.000000	31.000000	6.000000	7.000000	60.000000	126.000000	102.000000	177.000000	4.000000	95.000000	15.000000	8.000000	83.000000	9.000000	10.000000	1.000000	53.000000	3.000000	1.000000	49.000000	2.000000	2.000000	60.750000	67.000000	0.200000	0.280093	0.600000	0.728902	0.308261	0.583333	0.588235	0.372843	0.451211	0.400000	0.542107	0.200000	0.090909	0.545455	0.201846	0.200000	0.463768	0.357902	26.888218	29.736729	21.255934	29.477773	23.888649	23.672853	24.622356	24.665516	23.759171	24.530643	25.097108	24.018127	23.974968	24.287565	19.615883	23.716012	23.327579	25.377644	23.974968	22.442814	22.874407	24.023522	23.953388
50%	0.000000	3.000000	1.000000	0.000000	6.800000	72.000000	23.000000	17.000000	78.000000	15.000000	12.000000	116.000000	261.500000	184.500000	351.500000	8.500000	205.000000	33.000000	22.000000	212.000000	22.000000	18.000000	1.000000	4.000000	1.000000	0.000000	9.341667	86.000000	27.000000	21.000000	88.000000	18.000000	19.000000	137.000000	292.000000	225.000000	392.000000	10.000000	222.500000	37.000000	26.000000	228.000000	26.000000	28.000000	5.000000	138.000000	10.000000	4.000000	134.000000	7.000000	8.000000	153.000000	166.000000	0.351351	0.343553	0.695652	0.819558	0.375000	0.685714	0.690778	0.436548	0.528110	0.588235	0.622117	0.286516	0.168831	0.605839	0.295268	0.292683	0.531034	0.444444	26.888218	49.352611	51.143720	29.477773	47.971515	47.863617	48.683643	48.079413	48.403107	48.252050	47.949935	47.906776	47.939145	49.222798	46.331463	48.252050	47.583082	48.640483	48.165732	46.719896	49.827363	48.230470	48.165732
75%	2.000000	8.000000	3.000000	1.000000	16.604167	180.000000	56.000000	43.000000	197.500000	37.250000	36.000000	282.000000	627.000000	429.000000	802.250000	21.000000	491.250000	80.250000	53.000000	511.000000	54.000000	53.000000	2.000000	9.000000	3.000000	1.000000	18.137500	174.000000	60.000000	45.000000	205.000000	40.000000	40.000000	280.250000	633.000000	423.000000	799.500000	23.000000	491.250000	88.000000	57.000000	511.250000	57.000000	58.000000	14.000000	313.250000	26.000000	11.000000	317.000000	18.000000	18.000000	350.250000	372.000000	0.500000	0.411043	0.777778	0.900000	0.437031	0.785714	0.792327	0.500000	0.605620	0.735294	0.679163	0.369705	0.250000	0.665761	0.386547	0.380952	0.589333	0.513450	76.542943	74.082866	74.126025	69.486405	72.777298	72.399655	72.464394	72.766508	72.502158	72.156884	72.701770	72.313336	72.248597	74.589810	73.003884	72.566897	72.529132	72.701770	72.680190	73.543375	74.492879	72.550712	72.550712
max	20.000000	84.000000	48.000000	13.000000	156.500000	1146.000000	594.000000	425.000000	1478.000000	453.000000	334.000000	1896.000000	3783.000000	2525.000000	4550.000000	274.000000	2891.000000	817.000000	559.000000	3392.000000	576.000000	535.000000	11.000000	59.000000	29.000000	12.000000	128.183333	1069.000000	444.000000	330.000000	1558.000000	259.000000	273.000000	1825.000000	4464.000000	2273.000000	5118.000000	131.000000	3475.000000	672.000000	400.000000	3940.000000	417.000000	420.000000	102.000000	2426.000000	258.000000	82.000000	2557.000000	162.000000	163.000000	2743.000000	2845.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.868852	100.000000	99.956841	100.000000	100.000000	99.956841	99.827363	99.956841	99.956841	99.913681	100.000000	99.784204	99.956841	99.870522	99.827288	99.827363	99.913681	99.956841	99.697885	99.956841	100.000000	100.000000	99.956841	99.956841

	KD	TD	SUB.ATT	REV	CTRL	HEAD	BODY	LEG	DISTANCE	CLINCH	GROUND	SIG	TOT.SIG	STR	TOTAL.STR	TOTAL.TD	TOTAL.HEAD	TOTAL.BODY	TOTAL.LEG	TOTAL.DISTANCE	TOTAL.CLINCH	TOTAL.GROUND	OPP_KD	OPP_TD	OPP_SUB.ATT	OPP_REV	OPP_CTRL	OPP_HEAD	OPP_BODY	OPP_LEG	OPP_DISTANCE	OPP_CLINCH	OPP_GROUND	OPP_SIG	OPP_TOT.SIG	OPP_STR	OPP_TOTAL.STR	OPP_TOTAL.TD	OPP_TOTAL.HEAD	OPP_TOTAL.BODY	OPP_TOTAL.LEG	OPP_TOTAL.DISTANCE	OPP_TOTAL.CLINCH	OPP_TOTAL.GROUND	TD_DEF	HEAD_DEF	BODY_DEF	LEG_DEF	DISTANCE_DEF	CLINCH_DEF	GROUND_DEF	SIG_DEF	STR_DEF	TD_%	HEAD_%	BODY_%	LEG_%	DISTANCE_%	CLINCH_%	GROUND_%	SIG_%	STR_%	TD_DEF_%	HEAD_DEF_%	BODY_DEF_%	LEG_DEF_%	DISTANCE_DEF_%	CLINCH_DEF_%	GROUND_DEF_%	SIG_DEF_%	STR_DEF_%	KD_PERCENTILE	TD_PERCENTILE	SUB.ATT_PERCENTILE	REV_PERCENTILE	CTRL_PERCENTILE	HEAD_PERCENTILE	BODY_PERCENTILE	LEG_PERCENTILE	DISTANCE_PERCENTILE	CLINCH_PERCENTILE	GROUND_PERCENTILE	SIG_PERCENTILE	STR_PERCENTILE	STR_%_PERCENTILE	TD_DEF_PERCENTILE	HEAD_DEF_PERCENTILE	BODY_DEF_PERCENTILE	LEG_DEF_PERCENTILE	DISTANCE_DEF_PERCENTILE	CLINCH_DEF_PERCENTILE	GROUND_DEF_PERCENTILE	SIG_DEF_PERCENTILE	STR_DEF_PERCENTILE
count	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.00000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	1866.000000	2085.000000	2009.000000	1971.000000	2087.000000	1956.000000	1758.000000	2092.000000	2095.000000	1911.000000	2090.000000	2027.000000	1969.000000	2086.000000	1964.000000	1941.000000	2093.000000	2094.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2095.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000	2096.000000
mean	1.058683	5.600191	2.057729	0.769084	11.627886	118.088263	39.458492	31.307252	137.583492	26.800095	24.47042	188.854008	430.268130	282.533397	537.821565	15.099237	333.883111	57.521947	38.863073	355.875000	38.735210	35.657920	1.218034	5.976145	2.235210	0.785782	12.490689	123.501908	40.728531	31.361164	140.180821	27.770515	27.640267	195.591603	436.952767	293.382156	549.156011	15.417939	339.630725	58.531489	38.790553	356.888836	39.771469	40.292462	9.441794	216.128817	17.802958	7.429389	216.708015	12.000954	12.652195	241.361164	255.773855	0.363409	0.341382	0.683110	0.794597	0.370691	0.664954	0.677831	0.434040	0.528742	0.554721	0.604784	0.289315	0.188770	0.604814	0.303448	0.297266	0.522517	0.433401	47.292138	47.342288	47.736600	48.657049	47.041820	46.740683	47.033542	47.230374	47.107032	46.936187	46.734743	46.769140	46.609702	49.425157	47.273533	46.876833	47.362220	47.257246	46.975671	47.182211	47.735251	46.888734	46.879159
std	1.854850	8.458207	3.500836	1.367931	15.023103	144.432066	52.294779	44.722481	178.861586	37.052445	35.74229	226.900015	511.715937	321.367907	611.521210	21.076804	400.202746	76.471896	55.432479	443.579615	51.602511	51.693459	1.599725	6.785173	3.056121	1.373256	13.050976	132.989145	45.593841	37.683759	168.602882	32.934249	30.651593	206.207487	482.027059	282.332654	561.447237	17.688175	380.467499	66.414012	46.291971	428.350204	46.757453	45.348051	12.143904	253.569742	22.560506	10.019445	264.682975	15.132478	16.353165	281.809804	290.978365	0.256233	0.123483	0.176488	0.175808	0.120782	0.197880	0.209698	0.121770	0.133631	0.264101	0.120664	0.157418	0.158293	0.113824	0.172052	0.170125	0.106305	0.119718	25.063888	27.812455	26.781905	24.960319	27.927503	27.601150	27.771251	27.901275	27.756811	27.869332	27.627323	27.597697	27.549108	29.171644	27.922652	27.705184	27.856337	27.674830	27.710033	27.888316	28.391933	27.699548	27.696789
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	26.888218	11.588261	21.255934	29.477773	2.546396	1.316357	2.740613	3.366422	1.143720	4.294346	8.610272	0.604230	0.258956	0.237478	7.466552	0.215796	4.661200	9.387139	0.388433	5.955978	6.689685	0.151057	0.107898
25%	0.000000	0.000000	0.000000	0.000000	1.833333	22.000000	7.000000	4.000000	25.000000	4.000000	2.00000	40.000000	98.000000	67.000000	136.000000	2.000000	73.000000	11.000000	6.000000	72.000000	7.000000	2.000000	0.000000	1.000000	0.000000	0.000000	3.333333	36.000000	9.750000	6.000000	29.000000	6.000000	6.000000	57.000000	119.750000	97.750000	168.750000	3.000000	91.000000	14.000000	7.000000	78.000000	8.000000	9.000000	1.000000	51.000000	3.000000	1.000000	46.000000	2.000000	2.000000	57.750000	64.000000	0.191244	0.278351	0.600000	0.727273	0.307692	0.576923	0.583333	0.371647	0.450787	0.394886	0.539619	0.200000	0.086957	0.544782	0.200000	0.200000	0.462069	0.355932	26.888218	11.588261	21.255934	29.477773	22.895986	22.852827	22.183858	21.514890	23.003884	22.183858	25.097108	22.809668	22.831247	24.114853	19.615883	22.982305	23.327579	25.377644	23.154942	22.442814	22.874407	22.966120	22.744929
50%	0.000000	3.000000	1.000000	0.000000	6.383333	67.000000	21.000000	16.000000	73.000000	14.000000	11.00000	107.000000	246.500000	169.500000	330.500000	8.000000	192.000000	31.000000	20.000000	199.000000	21.000000	16.000000	1.000000	4.000000	1.000000	0.000000	8.900000	81.000000	25.000000	19.000000	81.500000	17.000000	18.000000	129.000000	274.000000	211.000000	366.000000	10.000000	208.000000	35.000000	24.000000	214.500000	24.000000	26.000000	5.000000	129.500000	10.000000	4.000000	126.000000	7.000000	7.000000	145.000000	157.000000	0.349415	0.342282	0.694656	0.822222	0.375000	0.686511	0.692308	0.436548	0.528724	0.588235	0.620780	0.286957	0.166667	0.605207	0.294118	0.294118	0.529459	0.443433	26.888218	49.352611	51.143720	29.477773	46.180406	46.137246	46.266724	46.568839	46.568839	46.719896	46.417782	46.094087	45.932240	49.352332	46.331463	46.331463	47.583082	48.640483	46.568839	46.719896	45.986189	46.353043	46.374622
75%	1.000000	7.000000	3.000000	1.000000	15.566667	162.250000	52.000000	40.000000	182.000000	34.000000	32.00000	258.000000	580.250000	382.000000	732.250000	20.000000	455.000000	75.250000	49.000000	475.000000	50.000000	48.000000	2.000000	8.000000	3.000000	1.000000	16.941667	162.000000	56.000000	43.000000	185.250000	37.000000	38.000000	257.000000	585.000000	391.000000	726.000000	21.000000	453.000000	81.000000	53.000000	473.250000	54.000000	54.000000	13.000000	292.000000	25.000000	10.000000	290.250000	16.000000	17.000000	324.000000	340.500000	0.500000	0.412429	0.781250	0.904304	0.438225	0.785714	0.797258	0.501828	0.605620	0.736842	0.679482	0.371665	0.253333	0.665761	0.388589	0.383721	0.589147	0.513450	62.580924	70.975399	74.126025	69.486405	70.608546	69.804704	70.608546	70.845921	70.306431	70.090634	69.788520	69.723781	69.507984	74.589810	71.126457	70.457488	71.385412	70.241692	70.274061	70.543807	72.637031	70.392749	70.220112
max	18.000000	84.000000	48.000000	13.000000	156.500000	1136.000000	594.000000	425.000000	1380.000000	453.000000	332.00000	1896.000000	3783.000000	2525.000000	4550.000000	231.000000	2872.000000	817.000000	559.000000	3392.000000	576.000000	429.000000	10.000000	59.000000	29.000000	12.000000	128.183333	1066.000000	414.000000	264.000000	1434.000000	259.000000	268.000000	1721.000000	4464.000000	2273.000000	5118.000000	131.000000	3475.000000	672.000000	317.000000	3940.000000	417.000000	389.000000	102.000000	2426.000000	258.000000	82.000000	2557.000000	162.000000	127.000000	2743.000000	2845.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.868852	99.935261	99.956841	100.000000	100.000000	99.956841	99.784204	99.956841	99.956841	99.827363	100.000000	99.741044	99.956841	99.870522	99.827288	99.827363	99.913681	99.956841	99.697885	99.956841	100.000000	99.784204	99.956841	99.956841

	FA_KD	FA_TD	FA_SUB.ATT	FA_REV	FA_CTRL	FA_HEAD	FA_BODY	FA_LEG	FA_DISTANCE	FA_CLINCH	FA_GROUND	FA_SIG	FA_TOT.SIG	FA_STR	FA_TOTAL.STR	FA_TOTAL.TD	FA_TOTAL.HEAD	FA_TOTAL.BODY	FA_TOTAL.LEG	FA_TOTAL.DISTANCE	FA_TOTAL.CLINCH	FA_TOTAL.GROUND	FA_OPP_KD	FA_OPP_TD	FA_OPP_SUB.ATT	FA_OPP_REV	FA_OPP_CTRL	FA_OPP_HEAD	FA_OPP_BODY	FA_OPP_LEG	FA_OPP_DISTANCE	FA_OPP_CLINCH	FA_OPP_GROUND	FA_OPP_SIG	FA_OPP_TOT.SIG	FA_OPP_STR	FA_OPP_TOTAL.STR	FA_OPP_TOTAL.TD	FA_OPP_TOTAL.HEAD	FA_OPP_TOTAL.BODY	FA_OPP_TOTAL.LEG	FA_OPP_TOTAL.DISTANCE	FA_OPP_TOTAL.CLINCH	FA_OPP_TOTAL.GROUND	FA_TD_DEF	FA_HEAD_DEF	FA_BODY_DEF	FA_LEG_DEF	FA_DISTANCE_DEF	FA_CLINCH_DEF	FA_GROUND_DEF	FA_SIG_DEF	FA_STR_DEF	FA_TD_%	FA_HEAD_%	FA_BODY_%	FA_LEG_%	FA_DISTANCE_%	FA_CLINCH_%	FA_GROUND_%	FA_SIG_%	FA_STR_%	FA_TD_DEF_%	FA_HEAD_DEF_%	FA_BODY_DEF_%	FA_LEG_DEF_%	FA_DISTANCE_DEF_%	FA_CLINCH_DEF_%	FA_GROUND_DEF_%	FA_SIG_DEF_%	FA_STR_DEF_%
count	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2317.000000	2085.000000	2306.000000	2229.000000	2191.000000	2308.000000	2176.000000	1977.000000	2313.000000	2316.000000	2130.000000	2311.000000	2247.000000	2188.000000	2307.000000	2185.000000	2162.000000	2314.000000	2315.000000
mean	0.102005	0.396189	0.177114	0.057451	0.828589	8.017597	2.657430	2.153952	9.093827	1.894024	1.841128	12.828979	29.160414	19.493507	36.800267	1.114328	22.583911	3.863829	2.712673	23.697608	2.770589	2.692217	0.157146	0.493745	0.248937	0.060147	1.033310	9.644342	2.930910	2.258015	9.783036	2.182145	2.868086	14.833268	31.625764	22.865865	40.828685	1.143396	24.696713	4.127037	2.802014	24.379967	3.094110	4.151687	0.649651	15.052371	1.196127	0.543998	14.596930	0.911965	1.283601	16.792496	17.962820	0.374723	0.349601	0.687569	0.795446	0.373780	0.670462	0.682669	0.440162	0.532381	0.558849	0.603835	0.289909	0.190255	0.604862	0.302909	0.293672	0.522411	0.435965
std	0.158727	0.407920	0.276405	0.114033	0.671398	4.857685	1.922465	1.803111	6.129980	1.674394	2.248926	6.915715	14.595553	9.171340	15.871864	0.966141	11.942925	2.677142	2.241967	14.067329	2.312388	3.253183	0.231004	0.455493	0.385061	0.131657	0.772006	5.057907	1.991133	1.757561	6.369895	2.051110	3.295818	6.781977	14.033882	9.551450	15.045011	0.844002	11.557293	2.634654	2.143185	14.365358	2.737444	4.811363	0.590317	7.853677	0.938833	0.612955	8.742281	0.919348	1.784963	8.580392	8.568811	0.254084	0.122528	0.174160	0.171992	0.118646	0.192223	0.201262	0.119060	0.129456	0.260741	0.117977	0.154262	0.155821	0.110411	0.167786	0.166646	0.102900	0.115474
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.066667	0.000000	0.000000	0.308565	4.500000	1.277778	0.850000	4.500000	0.729167	0.333333	7.952381	19.000000	13.625000	26.666667	0.363333	14.211111	1.952381	1.050000	13.153846	1.166667	0.500000	0.000000	0.166667	0.000000	0.000000	0.473611	6.300000	1.600000	1.000000	5.160000	0.916667	0.863636	10.166667	22.080952	16.900000	31.111111	0.566667	16.861111	2.333333	1.333333	14.166667	1.333333	1.219444	0.230769	9.583333	0.541667	0.125000	8.416667	0.333333	0.250000	10.833333	12.000000	0.200000	0.285714	0.608696	0.729079	0.313344	0.589991	0.596774	0.377717	0.457627	0.400000	0.540541	0.202106	0.088062	0.546983	0.202899	0.193548	0.465675	0.367468
50%	0.000000	0.300000	0.083333	0.000000	0.700000	7.605333	2.333333	1.806250	8.186667	1.500000	1.285714	12.333333	28.000000	18.916667	35.833333	0.952381	21.500000	3.437500	2.243137	22.106061	2.266667	1.888889	0.071429	0.391304	0.119048	0.000000	0.885897	8.833333	2.658333	2.000000	8.904167	1.666667	1.944444	14.015152	30.222727	21.466667	39.508333	1.000000	23.316667	3.783333	2.439394	22.722222	2.500000	2.791667	0.533333	13.928571	1.029412	0.404762	13.481481	0.708333	0.744444	15.666667	16.888889	0.368421	0.355643	0.700000	0.821429	0.379855	0.692308	0.697674	0.442922	0.533225	0.595865	0.619916	0.288136	0.171793	0.607275	0.294118	0.291253	0.531250	0.445578
75%	0.166667	0.592857	0.250000	0.076923	1.208333	10.717949	3.605556	3.000000	12.533333	2.607143	2.622222	17.000000	38.500000	24.833333	46.764706	1.625000	29.762222	5.277778	3.791667	32.416667	3.777778	3.729167	0.222222	0.694444	0.333333	0.083333	1.428056	12.166667	3.864444	3.000000	13.214286	2.904762	3.583333	18.500000	39.444444	27.470833	49.333333	1.555556	31.250000	5.416667	3.825397	32.688095	4.041667	5.333333	0.930556	19.491667	1.666667	0.777778	19.666667	1.183333	1.571429	21.666667	22.854762	0.500000	0.423267	0.785714	0.904762	0.440638	0.786593	0.796610	0.509091	0.607611	0.731799	0.678351	0.372035	0.255910	0.662386	0.386777	0.378378	0.586715	0.514752
max	1.000000	3.500000	3.000000	1.500000	4.891667	35.000000	15.666667	21.000000	45.333333	15.000000	32.000000	49.666667	98.333333	75.000000	105.000000	7.333333	80.333333	18.777778	24.000000	95.500000	20.166667	49.000000	2.000000	5.000000	5.000000	2.000000	4.700000	38.500000	21.000000	20.500000	54.000000	20.500000	28.000000	60.000000	100.000000	85.333333	112.000000	7.333333	82.750000	30.833333	24.500000	98.333333	30.500000	39.000000	6.000000	72.000000	11.833333	9.000000	73.000000	12.000000	19.000000	73.333333	73.333333	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.868852

	Total_Rounds	Total_Fights	Wins	Losses	Draws	No_Contests	Win_by_KO_TKO	Win_by_Submission	Win_by_Decision	Loss_by_KO_TKO	Loss_by_Submission	Loss_by_Decision	Total_Fights_with_Title	Wins_with_Title	Losses_with_Title	Draws_with_Title	No_Contests_with_Title	Win_by_KO_TKO_with_Title	Win_by_Submission_with_Title	Win_by_Decision_with_Title	Loss_by_KO_TKO_with_Title	Loss_by_Submission_with_Title	Loss_by_Decision_with_Title	Win_%	Loss_%	KO_Win_%	Sub_Win_%	Dec_Win_%	KO_Loss_%	Sub_Loss_%	Dec_Loss_%	Title_Win_%	Title_Loss_%	Title_KO_Win_%	Title_Sub_Win_%	Title_Dec_Win_%	Title_KO_Loss_%	Title_Sub_Loss_%	Title_Dec_Loss_%	Rounds_per_Fight
count	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	2304.000000	210.000000	210.000000	210.000000	210.000000	210.000000	210.000000	210.000000	210.000000	2304.000000
mean	14.623264	6.171875	3.003472	3.057726	0.046441	0.064236	0.994358	0.583767	1.417101	1.013889	0.601997	1.433160	0.262153	0.121962	0.134549	0.005208	0.000434	0.053819	0.021267	0.046441	0.057726	0.023438	0.052951	36.323768	61.847029	11.652643	7.121023	17.380853	21.405818	13.572657	26.656872	28.217670	69.809507	11.886020	5.268759	10.967653	29.201299	13.230365	27.258795	2.269684
std	14.554776	5.640170	3.706912	2.331357	0.220557	0.255630	1.705006	1.192288	2.041490	1.223513	0.923949	1.584459	1.173906	0.755564	0.518142	0.071996	0.020833	0.374691	0.214496	0.356096	0.298587	0.170227	0.291387	29.225672	29.728415	17.879819	13.620865	21.137844	28.102144	23.956558	28.299674	33.643181	34.679223	20.683441	14.025604	21.721206	37.584210	29.789935	38.165220	0.599040
min	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	4.000000	2.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	40.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	40.714286	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000
50%	9.000000	4.000000	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	37.500000	60.000000	0.000000	0.000000	11.111111	11.437908	0.000000	20.000000	0.000000	100.000000	0.000000	0.000000	0.000000	9.545455	0.000000	0.000000	2.333333
75%	20.000000	8.000000	4.000000	4.000000	0.000000	0.000000	1.000000	1.000000	2.000000	1.250000	1.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	57.142857	100.000000	20.000000	11.111111	31.250000	33.333333	20.000000	42.857143	50.000000	100.000000	20.000000	0.000000	16.666667	50.000000	0.000000	50.000000	2.724922
max	111.000000	41.000000	23.000000	18.000000	2.000000	2.000000	14.000000	16.000000	14.000000	8.000000	7.000000	11.000000	14.000000	12.000000	6.000000	1.000000	1.000000	5.000000	5.000000	6.000000	6.000000	3.000000	5.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	75.000000	100.000000	100.000000	100.000000	100.000000	4.000000

	Total_Rounds	Total_Fights	Wins	Losses	Draws	No_Contests	Win_by_KO_TKO	Win_by_Submission	Win_by_Decision	Loss_by_KO_TKO	Loss_by_Submission	Loss_by_Decision	Total_Fights_with_Title	Wins_with_Title	Losses_with_Title	Draws_with_Title	No_Contests_with_Title	Win_by_KO_TKO_with_Title	Win_by_Submission_with_Title	Win_by_Decision_with_Title	Loss_by_KO_TKO_with_Title	Loss_by_Submission_with_Title	Loss_by_Decision_with_Title	Win_%	Loss_%	KO_Win_%	Sub_Win_%	Dec_Win_%	KO_Loss_%	Sub_Loss_%	Dec_Loss_%	Title_Win_%	Title_Loss_%	Title_KO_Win_%	Title_Sub_Win_%	Title_Dec_Win_%	Title_KO_Loss_%	Title_Sub_Loss_%	Title_Dec_Loss_%	Rounds_per_Fight
count	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000	213.000000
mean	39.657277	15.150235	9.469484	5.460094	0.122066	0.098592	3.638498	1.676056	4.131455	2.037559	0.812207	2.600939	3.089202	1.521127	1.502347	0.056338	0.009390	0.657277	0.267606	0.591549	0.643192	0.262911	0.591549	61.308590	37.258460	24.026060	10.679770	26.484512	14.517436	5.639074	17.045424	28.978448	69.047172	12.364603	5.305903	11.214045	28.471005	12.566713	27.892083	2.583171
std	19.794460	6.967984	4.881159	3.050604	0.368745	0.298815	2.836059	2.174735	2.955902	1.845059	1.108476	2.247724	3.072478	2.552427	0.979143	0.231116	0.096672	1.216981	0.738901	1.294883	0.803538	0.519783	0.799365	15.596605	15.593465	16.740287	11.575076	16.264009	13.644809	7.905501	14.018623	33.492435	34.532522	21.022367	13.716553	21.156069	37.177658	28.624965	38.650714	0.546618
min	4.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	23.000000	10.000000	6.000000	3.000000	0.000000	0.000000	1.000000	0.000000	2.000000	1.000000	0.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	53.846154	28.571429	12.500000	0.000000	14.285714	5.882353	0.000000	7.142857	0.000000	40.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.250000
50%	38.000000	14.000000	9.000000	5.000000	0.000000	0.000000	3.000000	1.000000	4.000000	2.000000	0.000000	2.000000	2.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	60.869565	36.842105	23.529412	7.692308	25.000000	12.500000	0.000000	15.000000	0.000000	80.000000	0.000000	0.000000	0.000000	8.333333	0.000000	0.000000	2.611111
75%	53.000000	20.000000	13.000000	7.000000	0.000000	0.000000	5.000000	2.000000	6.000000	3.000000	1.000000	4.000000	4.000000	2.000000	2.000000	0.000000	0.000000	1.000000	0.000000	1.000000	1.000000	0.000000	1.000000	70.000000	45.454545	33.333333	17.647059	37.500000	20.000000	9.090909	25.000000	55.555556	100.000000	20.000000	0.000000	16.666667	50.000000	0.000000	50.000000	2.923077
max	111.000000	41.000000	23.000000	17.000000	2.000000	1.000000	14.000000	16.000000	12.000000	8.000000	5.000000	10.000000	16.000000	15.000000	6.000000	1.000000	1.000000	7.000000	5.000000	8.000000	6.000000	3.000000	5.000000	100.000000	100.000000	75.000000	50.000000	68.750000	80.000000	42.857143	80.000000	100.000000	100.000000	100.000000	75.000000	100.000000	100.000000	100.000000	100.000000	3.833333

	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	Buyrate_PERCENTILE	PPV_Fights_PERCENTILE
count	8.230000e+02	8.230000e+02	823.000000	823.000000	823.000000
mean	2.313442e+06	4.825095e+05	4.780073	79.756446	70.260492
std	1.993544e+06	2.322253e+05	3.604403	13.905212	16.825467
min	7.500000e+04	3.750000e+04	2.000000	41.324989	49.963821
25%	9.450000e+05	3.333333e+05	2.000000	70.576176	49.963821
50%	1.685000e+06	4.625000e+05	3.000000	81.916271	65.484805
75%	3.077500e+06	5.944286e+05	6.000000	91.119983	85.419682
max	1.355517e+07	1.630000e+06	22.000000	100.000000	99.927641

	Buyrate	Avg_Buyrate_Per_Event	PPV_Fights	Buyrate_PERCENTILE	PPV_Fights_PERCENTILE
Buyrate	1.000000	0.412245	0.867996	0.803415	0.758139
Avg_Buyrate_Per_Event	0.412245	1.000000	0.008387	0.612128	0.016307
PPV_Fights	0.867996	0.008387	1.000000	0.662269	0.860752
Buyrate_PERCENTILE	0.803415	0.612128	0.662269	1.000000	0.745089
PPV_Fights_PERCENTILE	0.758139	0.016307	0.860752	0.745089	1.000000

	Win_%	Total_Fights	Cluster
0	100.000000	1	2
1	50.000000	6	1
2	72.222222	18	2
3	85.714286	7	2
4	71.428571	7	2